[{"data":1,"prerenderedAt":19872},["ShallowReactive",2],{"blog-post-machine-learning-paradigms-and-mathematical-foundations-en":3,"blog-post-adjacent-machine-learning-paradigms-and-mathematical-foundations-en":17425},{"id":4,"title":5,"author":6,"body":7,"date":17406,"description":13,"extension":17407,"image":17408,"lastmod":17406,"meta":17409,"navigation":17410,"order":11114,"path":17411,"seo":17412,"sitemap":17413,"slug":17416,"stem":17417,"summary":17418,"tags":17419,"__hash__":17424},"content_en\u002Fblog\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations.md","Machine learning paradigms and mathematical foundations","David Deras",{"type":8,"value":9,"toc":17389},"minimark",[10,14,24,27,30,35,38,43,46,647,737,740,757,759,763,972,986,988,992,995,1022,1025,1027,1031,1041,1044,1047,1050,1532,1535,2157,2219,2330,2429,2440,2443,2514,2554,2821,2824,3162,3165,3600,3602,3881,3884,4184,4187,4563,4605,4611,4790,5378,5619,5621,5931,5934,5978,5985,6170,6172,6411,6553,7128,7365,7367,7675,7678,8284,8642,8832,8957,9059,9062,9220,9227,9629,9631,9850,9855,10444,10450,10693,11112,11118,11417,11453,11464,11466,11469,11472,11475,11495,11500,11503,11625,11750,11757,11766,12216,12225,12419,12424,13050,13052,13593,13596,13601,13604,13793,13795,14350,14403,14406,14549,14556,14700,15001,15014,15017,15337,15340,15968,15970,16247,16250,16261,16264,16272,16275,16280,16555,16560,16855,16858,16862,16866,16872,16892,16896,16922,16927,16931,16938,16941,17048,17050,17168,17171,17270,17273,17282,17296,17299,17326,17330,17333],[11,12,13],"p",{},"We continue learning about Machine Learning, this time we will delve into the different paradigms of machine learning and the mathematical foundations that support these models.",[11,15,16,17],{},"Previous article: ",[18,19,23],"a",{"href":20,"rel":21},"https:\u002F\u002Fderas.dev\u002Fblog\u002Fmachine-learning-fundamentals",[22],"nofollow","Machine Learning Fundamentals",[25,26],"table-of-contents",{},[28,29],"hr",{},[31,32,34],"h2",{"id":33},"machine-learning-paradigms","Machine Learning Paradigms",[11,36,37],{},"In the previous article, we already mentioned the types of machine learning, in this section we will review them and then move on to the mathematical foundations.",[39,40,42],"h3",{"id":41},"supervised-learning","Supervised Learning",[11,44,45],{},"Supervised learning requires a dataset where each input example X is associated with a label or output Y. The objective of the model is to learn a function that maps inputs to correct outputs.",[11,47,48,49,500,501,574,575,646],{},"Based on this, the training dataset is represented as a set of pairs ",[50,51,54,161],"span",{"className":52},[53],"katex",[50,55,58],{"className":56},[57],"katex-mathml",[59,60,62],"math",{"xmlns":61},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[63,64,65,156],"semantics",{},[66,67,68,73,76,87,91,98,101,103,105,112,114,120,122,124,128,130,132,134,136,143,145,151,153],"mrow",{},[69,70,72],"mo",{"stretchy":71},"false","{",[69,74,75],{"stretchy":71},"(",[77,78,79,83],"msub",{},[80,81,82],"mi",{},"x",[84,85,86],"mn",{},"1",[69,88,90],{"separator":89},"true",",",[77,92,93,96],{},[80,94,95],{},"y",[84,97,86],{},[69,99,100],{"stretchy":71},")",[69,102,90],{"separator":89},[69,104,75],{"stretchy":71},[77,106,107,109],{},[80,108,82],{},[84,110,111],{},"2",[69,113,90],{"separator":89},[77,115,116,118],{},[80,117,95],{},[84,119,111],{},[69,121,100],{"stretchy":71},[69,123,90],{"separator":89},[80,125,127],{"mathvariant":126},"normal",".",[80,129,127],{"mathvariant":126},[80,131,127],{"mathvariant":126},[69,133,90],{"separator":89},[69,135,75],{"stretchy":71},[77,137,138,140],{},[80,139,82],{},[80,141,142],{},"n",[69,144,90],{"separator":89},[77,146,147,149],{},[80,148,95],{},[80,150,142],{},[69,152,100],{"stretchy":71},[69,154,155],{"stretchy":71},"}",[157,158,160],"annotation",{"encoding":159},"application\u002Fx-tex","\\{(x_1, y_1), (x_2, y_2), ..., (x_n, y_n)\\}",[50,162,165],{"className":163,"ariaHidden":89},[164],"katex-html",[50,166,169,174,179,237,241,246,288,292,295,298,301,341,344,347,387,390,393,396,400,403,406,409,450,453,456,496],{"className":167},[168],"base",[50,170],{"className":171,"style":173},[172],"strut","height:1em;vertical-align:-0.25em;",[50,175,178],{"className":176},[177],"mopen","{(",[50,180,183,187],{"className":181},[182],"mord",[50,184,82],{"className":185},[182,186],"mathnormal",[50,188,191],{"className":189},[190],"msupsub",[50,192,196,228],{"className":193},[194,195],"vlist-t","vlist-t2",[50,197,200,223],{"className":198},[199],"vlist-r",[50,201,205],{"className":202,"style":204},[203],"vlist","height:0.3011em;",[50,206,208,213],{"style":207},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[50,209],{"className":210,"style":212},[211],"pstrut","height:2.7em;",[50,214,220],{"className":215},[216,217,218,219],"sizing","reset-size6","size3","mtight",[50,221,86],{"className":222},[182,219],[50,224,227],{"className":225},[226],"vlist-s","​",[50,229,231],{"className":230},[199],[50,232,235],{"className":233,"style":234},[203],"height:0.15em;",[50,236],{},[50,238,90],{"className":239},[240],"mpunct",[50,242],{"className":243,"style":245},[244],"mspace","margin-right:0.1667em;",[50,247,249,253],{"className":248},[182],[50,250,95],{"className":251,"style":252},[182,186],"margin-right:0.0359em;",[50,254,256],{"className":255},[190],[50,257,259,280],{"className":258},[194,195],[50,260,262,277],{"className":261},[199],[50,263,265],{"className":264,"style":204},[203],[50,266,268,271],{"style":267},"top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;",[50,269],{"className":270,"style":212},[211],[50,272,274],{"className":273},[216,217,218,219],[50,275,86],{"className":276},[182,219],[50,278,227],{"className":279},[226],[50,281,283],{"className":282},[199],[50,284,286],{"className":285,"style":234},[203],[50,287],{},[50,289,100],{"className":290},[291],"mclose",[50,293,90],{"className":294},[240],[50,296],{"className":297,"style":245},[244],[50,299,75],{"className":300},[177],[50,302,304,307],{"className":303},[182],[50,305,82],{"className":306},[182,186],[50,308,310],{"className":309},[190],[50,311,313,333],{"className":312},[194,195],[50,314,316,330],{"className":315},[199],[50,317,319],{"className":318,"style":204},[203],[50,320,321,324],{"style":207},[50,322],{"className":323,"style":212},[211],[50,325,327],{"className":326},[216,217,218,219],[50,328,111],{"className":329},[182,219],[50,331,227],{"className":332},[226],[50,334,336],{"className":335},[199],[50,337,339],{"className":338,"style":234},[203],[50,340],{},[50,342,90],{"className":343},[240],[50,345],{"className":346,"style":245},[244],[50,348,350,353],{"className":349},[182],[50,351,95],{"className":352,"style":252},[182,186],[50,354,356],{"className":355},[190],[50,357,359,379],{"className":358},[194,195],[50,360,362,376],{"className":361},[199],[50,363,365],{"className":364,"style":204},[203],[50,366,367,370],{"style":267},[50,368],{"className":369,"style":212},[211],[50,371,373],{"className":372},[216,217,218,219],[50,374,111],{"className":375},[182,219],[50,377,227],{"className":378},[226],[50,380,382],{"className":381},[199],[50,383,385],{"className":384,"style":234},[203],[50,386],{},[50,388,100],{"className":389},[291],[50,391,90],{"className":392},[240],[50,394],{"className":395,"style":245},[244],[50,397,399],{"className":398},[182],"...",[50,401,90],{"className":402},[240],[50,404],{"className":405,"style":245},[244],[50,407,75],{"className":408},[177],[50,410,412,415],{"className":411},[182],[50,413,82],{"className":414},[182,186],[50,416,418],{"className":417},[190],[50,419,421,442],{"className":420},[194,195],[50,422,424,439],{"className":423},[199],[50,425,428],{"className":426,"style":427},[203],"height:0.1514em;",[50,429,430,433],{"style":207},[50,431],{"className":432,"style":212},[211],[50,434,436],{"className":435},[216,217,218,219],[50,437,142],{"className":438},[182,186,219],[50,440,227],{"className":441},[226],[50,443,445],{"className":444},[199],[50,446,448],{"className":447,"style":234},[203],[50,449],{},[50,451,90],{"className":452},[240],[50,454],{"className":455,"style":245},[244],[50,457,459,462],{"className":458},[182],[50,460,95],{"className":461,"style":252},[182,186],[50,463,465],{"className":464},[190],[50,466,468,488],{"className":467},[194,195],[50,469,471,485],{"className":470},[199],[50,472,474],{"className":473,"style":427},[203],[50,475,476,479],{"style":267},[50,477],{"className":478,"style":212},[211],[50,480,482],{"className":481},[216,217,218,219],[50,483,142],{"className":484},[182,186,219],[50,486,227],{"className":487},[226],[50,489,491],{"className":490},[199],[50,492,494],{"className":493,"style":234},[203],[50,495],{},[50,497,499],{"className":498},[291],")}",", where ",[50,502,504,523],{"className":503},[53],[50,505,507],{"className":506},[57],[59,508,509],{"xmlns":61},[63,510,511,520],{},[66,512,513],{},[77,514,515,517],{},[80,516,82],{},[80,518,519],{},"i",[157,521,522],{"encoding":159},"x_i",[50,524,526],{"className":525,"ariaHidden":89},[164],[50,527,529,533],{"className":528},[168],[50,530],{"className":531,"style":532},[172],"height:0.5806em;vertical-align:-0.15em;",[50,534,536,539],{"className":535},[182],[50,537,82],{"className":538},[182,186],[50,540,542],{"className":541},[190],[50,543,545,566],{"className":544},[194,195],[50,546,548,563],{"className":547},[199],[50,549,552],{"className":550,"style":551},[203],"height:0.3117em;",[50,553,554,557],{"style":207},[50,555],{"className":556,"style":212},[211],[50,558,560],{"className":559},[216,217,218,219],[50,561,519],{"className":562},[182,186,219],[50,564,227],{"className":565},[226],[50,567,569],{"className":568},[199],[50,570,572],{"className":571,"style":234},[203],[50,573],{}," is the input and ",[50,576,578,596],{"className":577},[53],[50,579,581],{"className":580},[57],[59,582,583],{"xmlns":61},[63,584,585,593],{},[66,586,587],{},[77,588,589,591],{},[80,590,95],{},[80,592,519],{},[157,594,595],{"encoding":159},"y_i",[50,597,599],{"className":598,"ariaHidden":89},[164],[50,600,602,606],{"className":601},[168],[50,603],{"className":604,"style":605},[172],"height:0.625em;vertical-align:-0.1944em;",[50,607,609,612],{"className":608},[182],[50,610,95],{"className":611,"style":252},[182,186],[50,613,615],{"className":614},[190],[50,616,618,638],{"className":617},[194,195],[50,619,621,635],{"className":620},[199],[50,622,624],{"className":623,"style":551},[203],[50,625,626,629],{"style":267},[50,627],{"className":628,"style":212},[211],[50,630,632],{"className":631},[216,217,218,219],[50,633,519],{"className":634},[182,186,219],[50,636,227],{"className":637},[226],[50,639,641],{"className":640},[199],[50,642,644],{"className":643,"style":234},[203],[50,645],{}," is the corresponding label. The supervised learning model attempts to find a function f that can predict the output Y from the input X:",[50,648,651],{"className":649},[650],"katex-display",[50,652,654,682],{"className":653},[53],[50,655,657],{"className":656},[57],[59,658,660],{"xmlns":61,"display":659},"block",[63,661,662,679],{},[66,663,664,667,670,673,676],{},[80,665,666],{},"f",[69,668,669],{},":",[80,671,672],{},"X",[69,674,675],{},"→",[80,677,678],{},"Y",[157,680,681],{"encoding":159},"f: X \\rightarrow Y",[50,683,685,707,727],{"className":684,"ariaHidden":89},[164],[50,686,688,692,696,700,704],{"className":687},[168],[50,689],{"className":690,"style":691},[172],"height:0.8889em;vertical-align:-0.1944em;",[50,693,666],{"className":694,"style":695},[182,186],"margin-right:0.1076em;",[50,697],{"className":698,"style":699},[244],"margin-right:0.2778em;",[50,701,669],{"className":702},[703],"mrel",[50,705],{"className":706,"style":699},[244],[50,708,710,714,718,721,724],{"className":709},[168],[50,711],{"className":712,"style":713},[172],"height:0.6833em;",[50,715,672],{"className":716,"style":717},[182,186],"margin-right:0.0785em;",[50,719],{"className":720,"style":699},[244],[50,722,675],{"className":723},[703],[50,725],{"className":726,"style":699},[244],[50,728,730,733],{"className":729},[168],[50,731],{"className":732,"style":713},[172],[50,734,678],{"className":735,"style":736},[182,186],"margin-right:0.2222em;",[11,738,739],{},"Depending on the type of output, supervised learning can be divided into:",[741,742,743,751],"ul",{},[744,745,746,750],"li",{},[747,748,749],"strong",{},"Classification",": When the output Y is a discrete category (that is, a categorical value). For example, classifying whether an image contains a cat or a dog, whether a number is even or odd, or whether an email is spam or not spam.",[744,752,753,756],{},[747,754,755],{},"Regression",": When the output Y is a continuous value (that is, a real number). For example, making predictions about future sales based on historical data, predicting the price of a house based on its features, or estimating the temperature of a city based on climatic factors.",[28,758],{},[39,760,762],{"id":761},"unsupervised-learning","Unsupervised Learning",[11,764,765,766,971],{},"In unsupervised learning, the model receives only the inputs X without associated labels: ",[50,767,769,815],{"className":768},[53],[50,770,772],{"className":771},[57],[59,773,774],{"xmlns":61},[63,775,776,812],{},[66,777,778,780,786,788,794,796,798,800,802,804,810],{},[69,779,72],{"stretchy":71},[77,781,782,784],{},[80,783,82],{},[84,785,86],{},[69,787,90],{"separator":89},[77,789,790,792],{},[80,791,82],{},[84,793,111],{},[69,795,90],{"separator":89},[80,797,127],{"mathvariant":126},[80,799,127],{"mathvariant":126},[80,801,127],{"mathvariant":126},[69,803,90],{"separator":89},[77,805,806,808],{},[80,807,82],{},[80,809,142],{},[69,811,155],{"stretchy":71},[157,813,814],{"encoding":159},"\\{x_1, x_2, ..., x_n\\}",[50,816,818],{"className":817,"ariaHidden":89},[164],[50,819,821,824,827,867,870,873,913,916,919,922,925,928,968],{"className":820},[168],[50,822],{"className":823,"style":173},[172],[50,825,72],{"className":826},[177],[50,828,830,833],{"className":829},[182],[50,831,82],{"className":832},[182,186],[50,834,836],{"className":835},[190],[50,837,839,859],{"className":838},[194,195],[50,840,842,856],{"className":841},[199],[50,843,845],{"className":844,"style":204},[203],[50,846,847,850],{"style":207},[50,848],{"className":849,"style":212},[211],[50,851,853],{"className":852},[216,217,218,219],[50,854,86],{"className":855},[182,219],[50,857,227],{"className":858},[226],[50,860,862],{"className":861},[199],[50,863,865],{"className":864,"style":234},[203],[50,866],{},[50,868,90],{"className":869},[240],[50,871],{"className":872,"style":245},[244],[50,874,876,879],{"className":875},[182],[50,877,82],{"className":878},[182,186],[50,880,882],{"className":881},[190],[50,883,885,905],{"className":884},[194,195],[50,886,888,902],{"className":887},[199],[50,889,891],{"className":890,"style":204},[203],[50,892,893,896],{"style":207},[50,894],{"className":895,"style":212},[211],[50,897,899],{"className":898},[216,217,218,219],[50,900,111],{"className":901},[182,219],[50,903,227],{"className":904},[226],[50,906,908],{"className":907},[199],[50,909,911],{"className":910,"style":234},[203],[50,912],{},[50,914,90],{"className":915},[240],[50,917],{"className":918,"style":245},[244],[50,920,399],{"className":921},[182],[50,923,90],{"className":924},[240],[50,926],{"className":927,"style":245},[244],[50,929,931,934],{"className":930},[182],[50,932,82],{"className":933},[182,186],[50,935,937],{"className":936},[190],[50,938,940,960],{"className":939},[194,195],[50,941,943,957],{"className":942},[199],[50,944,946],{"className":945,"style":427},[203],[50,947,948,951],{"style":207},[50,949],{"className":950,"style":212},[211],[50,952,954],{"className":953},[216,217,218,219],[50,955,142],{"className":956},[182,186,219],[50,958,227],{"className":959},[226],[50,961,963],{"className":962},[199],[50,964,966],{"className":965,"style":234},[203],[50,967],{},[50,969,155],{"className":970},[291],". The objective is to find underlying patterns or structures in the data.\nSome examples of unsupervised learning techniques include:",[741,973,974,980],{},[744,975,976,979],{},[747,977,978],{},"Clustering",": Grouping similar data points into clusters. For example, segmenting customers into groups based on their purchasing behaviors.",[744,981,982,985],{},[747,983,984],{},"Dimensionality Reduction",": Reducing the number of variables in a dataset while preserving as much information as possible. For example, using PCA (Principal Component Analysis) to visualize data in 2D or 3D.",[28,987],{},[39,989,991],{"id":990},"reinforcement-learning","Reinforcement Learning",[11,993,994],{},"Reinforcement learning is based on the idea that an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on the actions it takes, and its objective is to maximize the accumulated reward over time.\nIn this paradigm, the agent learns an action policy that allows it to make optimal decisions based on the current state of the environment. A classic example of reinforcement learning is the game of chess, where the agent learns to play better as it plays more games and receives feedback about its moves.",[996,997,998,1004,1010,1016],"ol",{},[744,999,1000,1003],{},[747,1001,1002],{},"Agent",": The system that makes decisions.",[744,1005,1006,1009],{},[747,1007,1008],{},"Environment",": The world with which the agent interacts.",[744,1011,1012,1015],{},[747,1013,1014],{},"Reward",": The feedback that the agent receives after taking an action.",[744,1017,1018,1021],{},[747,1019,1020],{},"Policy",": The strategy that the agent follows to make decisions.",[11,1023,1024],{},"Its applications include games, robotics, and recommendation systems.",[28,1026],{},[31,1028,1030],{"id":1029},"mathematical-foundations","Mathematical Foundations",[11,1032,1033,1034,1037,1038,127],{},"It is important to understand that machine learning is based on concepts from linear algebra, calculus, probability, and statistics. These fundamentals are essential for understanding how ",[747,1035,1036],{},"models work"," and how they are ",[747,1039,1040],{},"optimized",[39,1042,755],{"id":1043},"regression",[11,1045,1046],{},"What is a regression problem? It is a type of problem where the objective is to predict a continuous value by finding the best line (or plane) that fits the data. We have already discussed application examples such as price calculation, sales forecasting, etc.",[11,1048,1049],{},"The simplest form of regression is linear regression, which can be mathematically expressed as:",[50,1051,1053],{"className":1052},[650],[50,1054,1056,1137],{"className":1055},[53],[50,1057,1059],{"className":1058},[57],[59,1060,1061],{"xmlns":61,"display":659},[63,1062,1063,1134],{},[66,1064,1065,1067,1070,1078,1081,1087,1093,1095,1101,1107,1109,1111,1113,1115,1117,1123,1129,1131],{},[80,1066,95],{},[69,1068,1069],{},"=",[77,1071,1072,1075],{},[80,1073,1074],{},"β",[84,1076,1077],{},"0",[69,1079,1080],{},"+",[77,1082,1083,1085],{},[80,1084,1074],{},[84,1086,86],{},[77,1088,1089,1091],{},[80,1090,82],{},[84,1092,86],{},[69,1094,1080],{},[77,1096,1097,1099],{},[80,1098,1074],{},[84,1100,111],{},[77,1102,1103,1105],{},[80,1104,82],{},[84,1106,111],{},[69,1108,1080],{},[80,1110,127],{"mathvariant":126},[80,1112,127],{"mathvariant":126},[80,1114,127],{"mathvariant":126},[69,1116,1080],{},[77,1118,1119,1121],{},[80,1120,1074],{},[80,1122,11],{},[77,1124,1125,1127],{},[80,1126,82],{},[80,1128,11],{},[69,1130,1080],{},[80,1132,1133],{},"ϵ",[157,1135,1136],{"encoding":159},"y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + ... + \\beta_p x_p + 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is the dependent variable (what we want to predict).",[744,1569,1570,1766],{},[50,1571,1573,1615],{"className":1572},[53],[50,1574,1576],{"className":1575},[57],[59,1577,1578],{"xmlns":61},[63,1579,1580,1612],{},[66,1581,1582,1588,1590,1596,1598,1600,1602,1604,1606],{},[77,1583,1584,1586],{},[80,1585,82],{},[84,1587,86],{},[69,1589,90],{"separator":89},[77,1591,1592,1594],{},[80,1593,82],{},[84,1595,111],{},[69,1597,90],{"separator":89},[80,1599,127],{"mathvariant":126},[80,1601,127],{"mathvariant":126},[80,1603,127],{"mathvariant":126},[69,1605,90],{"separator":89},[77,1607,1608,1610],{},[80,1609,82],{},[80,1611,11],{},[157,1613,1614],{"encoding":159},"x_1, x_2, ..., x_p",[50,1616,1618],{"className":1617,"ariaHidden":89},[164],[50,1619,1621,1625,1665,1668,1671,1711,1714,1717,1720,1723,1726],{"className":1620},[168],[50,1622],{"className":1623,"style":1624},[172],"height:0.7167em;vertical-align:-0.2861em;",[50,1626,1628,1631],{"className":1627},[182],[50,1629,82],{"className":1630},[182,186],[50,1632,1634],{"className":1633},[190],[50,1635,1637,1657],{"className":1636},[194,195],[50,1638,1640,1654],{"className":1639},[199],[50,1641,1643],{"className":1642,"style":204},[203],[50,1644,1645,1648],{"style":207},[50,1646],{"className":1647,"style":212},[211],[50,1649,1651],{"className":1650},[216,217,218,219],[50,1652,86],{"className":1653},[182,219],[50,1655,227],{"className":1656},[226],[50,1658,1660],{"className":1659},[199],[50,1661,1663],{"className":1662,"style":234},[203],[50,1664],{},[50,1666,90],{"className":1667},[240],[50,1669],{"className":1670,"style":245},[244],[50,1672,1674,1677],{"className":1673},[182],[50,1675,82],{"className":1676},[182,186],[50,1678,1680],{"className":1679},[190],[50,1681,1683,1703],{"className":1682},[194,195],[50,1684,1686,1700],{"className":1685},[199],[50,1687,1689],{"className":1688,"style":204},[203],[50,1690,1691,1694],{"style":207},[50,1692],{"className":1693,"style":212},[211],[50,1695,1697],{"className":1696},[216,217,218,219],[50,1698,111],{"className":1699},[182,219],[50,1701,227],{"className":1702},[226],[50,1704,1706],{"className":1705},[199],[50,1707,1709],{"className":1708,"style":234},[203],[50,1710],{},[50,1712,90],{"className":1713},[240],[50,1715],{"className":1716,"style":245},[244],[50,1718,399],{"className":1719},[182],[50,1721,90],{"className":1722},[240],[50,1724],{"className":1725,"style":245},[244],[50,1727,1729,1732],{"className":1728},[182],[50,1730,82],{"className":1731},[182,186],[50,1733,1735],{"className":1734},[190],[50,1736,1738,1758],{"className":1737},[194,195],[50,1739,1741,1755],{"className":1740},[199],[50,1742,1744],{"className":1743,"style":427},[203],[50,1745,1746,1749],{"style":207},[50,1747],{"className":1748,"style":212},[211],[50,1750,1752],{"className":1751},[216,217,218,219],[50,1753,11],{"className":1754},[182,186,219],[50,1756,227],{"className":1757},[226],[50,1759,1761],{"className":1760},[199],[50,1762,1764],{"className":1763,"style":1470},[203],[50,1765],{}," are the independent variables (the features).",[744,1768,1769,1839,1840,1868,1869,1897],{},[50,1770,1772,1790],{"className":1771},[53],[50,1773,1775],{"className":1774},[57],[59,1776,1777],{"xmlns":61},[63,1778,1779,1787],{},[66,1780,1781],{},[77,1782,1783,1785],{},[80,1784,1074],{},[84,1786,1077],{},[157,1788,1789],{"encoding":159},"\\beta_0",[50,1791,1793],{"className":1792,"ariaHidden":89},[164],[50,1794,1796,1799],{"className":1795},[168],[50,1797],{"className":1798,"style":691},[172],[50,1800,1802,1805],{"className":1801},[182],[50,1803,1074],{"className":1804,"style":1170},[182,186],[50,1806,1808],{"className":1807},[190],[50,1809,1811,1831],{"className":1810},[194,195],[50,1812,1814,1828],{"className":1813},[199],[50,1815,1817],{"className":1816,"style":204},[203],[50,1818,1819,1822],{"style":1185},[50,1820],{"className":1821,"style":212},[211],[50,1823,1825],{"className":1824},[216,217,218,219],[50,1826,1077],{"className":1827},[182,219],[50,1829,227],{"className":1830},[226],[50,1832,1834],{"className":1833},[199],[50,1835,1837],{"className":1836,"style":234},[203],[50,1838],{}," is the intercept (the value of ",[50,1841,1843,1856],{"className":1842},[53],[50,1844,1846],{"className":1845},[57],[59,1847,1848],{"xmlns":61},[63,1849,1850,1854],{},[66,1851,1852],{},[80,1853,95],{},[157,1855,95],{"encoding":159},[50,1857,1859],{"className":1858,"ariaHidden":89},[164],[50,1860,1862,1865],{"className":1861},[168],[50,1863],{"className":1864,"style":605},[172],[50,1866,95],{"className":1867,"style":252},[182,186]," when all ",[50,1870,1872,1885],{"className":1871},[53],[50,1873,1875],{"className":1874},[57],[59,1876,1877],{"xmlns":61},[63,1878,1879,1883],{},[66,1880,1881],{},[80,1882,82],{},[157,1884,82],{"encoding":159},[50,1886,1888],{"className":1887,"ariaHidden":89},[164],[50,1889,1891,1894],{"className":1890},[168],[50,1892],{"className":1893,"style":1528},[172],[50,1895,82],{"className":1896},[182,186]," are 0).",[744,1899,1900,2095],{},[50,1901,1903,1945],{"className":1902},[53],[50,1904,1906],{"className":1905},[57],[59,1907,1908],{"xmlns":61},[63,1909,1910,1942],{},[66,1911,1912,1918,1920,1926,1928,1930,1932,1934,1936],{},[77,1913,1914,1916],{},[80,1915,1074],{},[84,1917,86],{},[69,1919,90],{"separator":89},[77,1921,1922,1924],{},[80,1923,1074],{},[84,1925,111],{},[69,1927,90],{"separator":89},[80,1929,127],{"mathvariant":126},[80,1931,127],{"mathvariant":126},[80,1933,127],{"mathvariant":126},[69,1935,90],{"separator":89},[77,1937,1938,1940],{},[80,1939,1074],{},[80,1941,11],{},[157,1943,1944],{"encoding":159},"\\beta_1, \\beta_2, ..., \\beta_p",[50,1946,1948],{"className":1947,"ariaHidden":89},[164],[50,1949,1951,1954,1994,1997,2000,2040,2043,2046,2049,2052,2055],{"className":1950},[168],[50,1952],{"className":1953,"style":1431},[172],[50,1955,1957,1960],{"className":1956},[182],[50,1958,1074],{"className":1959,"style":1170},[182,186],[50,1961,1963],{"className":1962},[190],[50,1964,1966,1986],{"className":1965},[194,195],[50,1967,1969,1983],{"className":1968},[199],[50,1970,1972],{"className":1971,"style":204},[203],[50,1973,1974,1977],{"style":1185},[50,1975],{"className":1976,"style":212},[211],[50,1978,1980],{"className":1979},[216,217,218,219],[50,1981,86],{"className":1982},[182,219],[50,1984,227],{"className":1985},[226],[50,1987,1989],{"className":1988},[199],[50,1990,1992],{"className":1991,"style":234},[203],[50,1993],{},[50,1995,90],{"className":1996},[240],[50,1998],{"className":1999,"style":245},[244],[50,2001,2003,2006],{"className":2002},[182],[50,2004,1074],{"className":2005,"style":1170},[182,186],[50,2007,2009],{"className":2008},[190],[50,2010,2012,2032],{"className":2011},[194,195],[50,2013,2015,2029],{"className":2014},[199],[50,2016,2018],{"className":2017,"style":204},[203],[50,2019,2020,2023],{"style":1185},[50,2021],{"className":2022,"style":212},[211],[50,2024,2026],{"className":2025},[216,217,218,219],[50,2027,111],{"className":2028},[182,219],[50,2030,227],{"className":2031},[226],[50,2033,2035],{"className":2034},[199],[50,2036,2038],{"className":2037,"style":234},[203],[50,2039],{},[50,2041,90],{"className":2042},[240],[50,2044],{"className":2045,"style":245},[244],[50,2047,399],{"className":2048},[182],[50,2050,90],{"className":2051},[240],[50,2053],{"className":2054,"style":245},[244],[50,2056,2058,2061],{"className":2057},[182],[50,2059,1074],{"className":2060,"style":1170},[182,186],[50,2062,2064],{"className":2063},[190],[50,2065,2067,2087],{"className":2066},[194,195],[50,2068,2070,2084],{"className":2069},[199],[50,2071,2073],{"className":2072,"style":427},[203],[50,2074,2075,2078],{"style":1185},[50,2076],{"className":2077,"style":212},[211],[50,2079,2081],{"className":2080},[216,217,218,219],[50,2082,11],{"className":2083},[182,186,219],[50,2085,227],{"className":2086},[226],[50,2088,2090],{"className":2089},[199],[50,2091,2093],{"className":2092,"style":1470},[203],[50,2094],{}," are the coefficients that represent the influence of each feature on the dependent variable.",[744,2097,2098,2127,2128,2156],{},[50,2099,2101,2115],{"className":2100},[53],[50,2102,2104],{"className":2103},[57],[59,2105,2106],{"xmlns":61},[63,2107,2108,2112],{},[66,2109,2110],{},[80,2111,1133],{},[157,2113,2114],{"encoding":159},"\\epsilon",[50,2116,2118],{"className":2117,"ariaHidden":89},[164],[50,2119,2121,2124],{"className":2120},[168],[50,2122],{"className":2123,"style":1528},[172],[50,2125,1133],{"className":2126},[182,186]," is the error or noise value, which represents the variability not explained by the model, that is, what affects ",[50,2129,2131,2144],{"className":2130},[53],[50,2132,2134],{"className":2133},[57],[59,2135,2136],{"xmlns":61},[63,2137,2138,2142],{},[66,2139,2140],{},[80,2141,95],{},[157,2143,95],{"encoding":159},[50,2145,2147],{"className":2146,"ariaHidden":89},[164],[50,2148,2150,2153],{"className":2149},[168],[50,2151],{"className":2152,"style":605},[172],[50,2154,95],{"className":2155,"style":252},[182,186]," but is not included in the independent variables.",[11,2158,2159,2160,2189,2190,2218],{},"The objective of the regression model is to find the values of the coefficients ",[50,2161,2163,2177],{"className":2162},[53],[50,2164,2166],{"className":2165},[57],[59,2167,2168],{"xmlns":61},[63,2169,2170,2174],{},[66,2171,2172],{},[80,2173,1074],{},[157,2175,2176],{"encoding":159},"\\beta",[50,2178,2180],{"className":2179,"ariaHidden":89},[164],[50,2181,2183,2186],{"className":2182},[168],[50,2184],{"className":2185,"style":691},[172],[50,2187,1074],{"className":2188,"style":1170},[182,186]," that minimize the difference between the model's predictions and the real values of ",[50,2191,2193,2206],{"className":2192},[53],[50,2194,2196],{"className":2195},[57],[59,2197,2198],{"xmlns":61},[63,2199,2200,2204],{},[66,2201,2202],{},[80,2203,95],{},[157,2205,95],{"encoding":159},[50,2207,2209],{"className":2208,"ariaHidden":89},[164],[50,2210,2212,2215],{"className":2211},[168],[50,2213],{"className":2214,"style":605},[172],[50,2216,95],{"className":2217,"style":252},[182,186],". This can be achieved using techniques such as the least squares method.",[11,2220,2221,2222,2225,2226,2264,2265,2298,2299,2329],{},"The ",[747,2223,2224],{},"intercept"," is represented as the first element of the coefficient vector ",[50,2227,2229,2244],{"className":2228},[53],[50,2230,2232],{"className":2231},[57],[59,2233,2234],{"xmlns":61},[63,2235,2236,2241],{},[66,2237,2238],{},[80,2239,1074],{"mathvariant":2240},"bold-italic",[157,2242,2243],{"encoding":159},"\\boldsymbol{\\beta}",[50,2245,2247],{"className":2246,"ariaHidden":89},[164],[50,2248,2250,2253],{"className":2249},[168],[50,2251],{"className":2252,"style":691},[172],[50,2254,2256],{"className":2255},[182],[50,2257,2259],{"className":2258},[182],[50,2260,1074],{"className":2261,"style":2263},[182,2262],"boldsymbol","margin-right:0.034em;",", and it is the value of ",[50,2266,2268,2283],{"className":2267},[53],[50,2269,2271],{"className":2270},[57],[59,2272,2273],{"xmlns":61},[63,2274,2275,2280],{},[66,2276,2277],{},[80,2278,95],{"mathvariant":2279},"bold",[157,2281,2282],{"encoding":159},"\\mathbf{y}",[50,2284,2286],{"className":2285,"ariaHidden":89},[164],[50,2287,2289,2293],{"className":2288},[168],[50,2290],{"className":2291,"style":2292},[172],"height:0.6389em;vertical-align:-0.1944em;",[50,2294,95],{"className":2295,"style":2297},[182,2296],"mathbf","margin-right:0.016em;"," when all the independent variables in ",[50,2300,2302,2316],{"className":2301},[53],[50,2303,2305],{"className":2304},[57],[59,2306,2307],{"xmlns":61},[63,2308,2309,2313],{},[66,2310,2311],{},[80,2312,672],{"mathvariant":2279},[157,2314,2315],{"encoding":159},"\\mathbf{X}",[50,2317,2319],{"className":2318,"ariaHidden":89},[164],[50,2320,2322,2326],{"className":2321},[168],[50,2323],{"className":2324,"style":2325},[172],"height:0.6861em;",[50,2327,672],{"className":2328},[182,2296]," are zero, that is, it represents the point where the regression line crosses the Y-axis.",[11,2331,2221,2332,2335,2336,2370,2371,2399,2400,2428],{},[747,2333,2334],{},"slope"," is represented by the remaining coefficients in the vector ",[50,2337,2339,2352],{"className":2338},[53],[50,2340,2342],{"className":2341},[57],[59,2343,2344],{"xmlns":61},[63,2345,2346,2350],{},[66,2347,2348],{},[80,2349,1074],{"mathvariant":2240},[157,2351,2243],{"encoding":159},[50,2353,2355],{"className":2354,"ariaHidden":89},[164],[50,2356,2358,2361],{"className":2357},[168],[50,2359],{"className":2360,"style":691},[172],[50,2362,2364],{"className":2363},[182],[50,2365,2367],{"className":2366},[182],[50,2368,1074],{"className":2369,"style":2263},[182,2262],", and each coefficient indicates the amount of change in the dependent variable ",[50,2372,2374,2387],{"className":2373},[53],[50,2375,2377],{"className":2376},[57],[59,2378,2379],{"xmlns":61},[63,2380,2381,2385],{},[66,2382,2383],{},[80,2384,95],{"mathvariant":2279},[157,2386,2282],{"encoding":159},[50,2388,2390],{"className":2389,"ariaHidden":89},[164],[50,2391,2393,2396],{"className":2392},[168],[50,2394],{"className":2395,"style":2292},[172],[50,2397,95],{"className":2398,"style":2297},[182,2296]," for each unit change in the corresponding independent variable in ",[50,2401,2403,2416],{"className":2402},[53],[50,2404,2406],{"className":2405},[57],[59,2407,2408],{"xmlns":61},[63,2409,2410,2414],{},[66,2411,2412],{},[80,2413,672],{"mathvariant":2279},[157,2415,2315],{"encoding":159},[50,2417,2419],{"className":2418,"ariaHidden":89},[164],[50,2420,2422,2425],{"className":2421},[168],[50,2423],{"className":2424,"style":2325},[172],[50,2426,672],{"className":2427},[182,2296],", keeping all other independent variables constant.\nGraphically we can see it as follows:",[11,2430,2431,2436],{},[2432,2433],"img",{"alt":2434,"src":2435},"Linear regression graph","\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations\u002Fshared\u002Fregression-model.webp",[2437,2438,2439],"em",{},"Linear Regression Graph",[11,2441,2442],{},"Each black point represents a training data point, and the red line represents the model.",[11,2444,2445,2446,2449,2450,2484,2485,2513],{},"The key point here is to understand that ",[747,2447,2448],{},"the main objective"," is to find the values of the coefficients ",[50,2451,2453,2466],{"className":2452},[53],[50,2454,2456],{"className":2455},[57],[59,2457,2458],{"xmlns":61},[63,2459,2460,2464],{},[66,2461,2462],{},[80,2463,1074],{"mathvariant":2240},[157,2465,2243],{"encoding":159},[50,2467,2469],{"className":2468,"ariaHidden":89},[164],[50,2470,2472,2475],{"className":2471},[168],[50,2473],{"className":2474,"style":691},[172],[50,2476,2478],{"className":2477},[182],[50,2479,2481],{"className":2480},[182],[50,2482,1074],{"className":2483,"style":2263},[182,2262]," that make the predictions ",[50,2486,2488,2501],{"className":2487},[53],[50,2489,2491],{"className":2490},[57],[59,2492,2493],{"xmlns":61},[63,2494,2495,2499],{},[66,2496,2497],{},[80,2498,95],{"mathvariant":2279},[157,2500,2282],{"encoding":159},[50,2502,2504],{"className":2503,"ariaHidden":89},[164],[50,2505,2507,2510],{"className":2506},[168],[50,2508],{"className":2509,"style":2292},[172],[50,2511,95],{"className":2512,"style":2297},[182,2296]," (the red line) as close as possible to the actual values.",[11,2515,2516,2517,2553],{},"That is, we do not seek to predict with perfect accuracy, as (in reality) there will always be some error or difference between what the model predicts and what actually happens. The term ",[50,2518,2520,2534],{"className":2519},[53],[50,2521,2523],{"className":2522},[57],[59,2524,2525],{"xmlns":61},[63,2526,2527,2531],{},[66,2528,2529],{},[80,2530,1133],{"mathvariant":2240},[157,2532,2533],{"encoding":159},"\\boldsymbol{\\epsilon}",[50,2535,2537],{"className":2536,"ariaHidden":89},[164],[50,2538,2540,2544],{"className":2539},[168],[50,2541],{"className":2542,"style":2543},[172],"height:0.4444em;",[50,2545,2547],{"className":2546},[182],[50,2548,2550],{"className":2549},[182],[50,2551,1133],{"className":2552},[182,2262]," is the difference between the values predicted by the model and the actual values:",[50,2555,2557],{"className":2556},[650],[50,2558,2560,2603],{"className":2559},[53],[50,2561,2563],{"className":2562},[57],[59,2564,2565],{"xmlns":61,"display":659},[63,2566,2567,2600],{},[66,2568,2569,2577,2579,2585,2588],{},[80,2570,2571],{},[77,2572,2573,2575],{},[80,2574,1133],{"mathvariant":2240},[80,2576,519],{"mathvariant":2240},[69,2578,1069],{},[77,2580,2581,2583],{},[80,2582,95],{"mathvariant":2279},[80,2584,519],{"mathvariant":2279},[69,2586,2587],{},"−",[2589,2590,2591,2597],"mover",{"accent":89},[77,2592,2593,2595],{},[80,2594,95],{"mathvariant":2279},[80,2596,519],{"mathvariant":2279},[69,2598,2599],{},"^",[157,2601,2602],{"encoding":159},"\\boldsymbol{\\epsilon _i} = \\mathbf{y_i} - \\hat{\\mathbf{y_i}}",[50,2604,2606,2669,2727],{"className":2605,"ariaHidden":89},[164],[50,2607,2609,2613,2660,2663,2666],{"className":2608},[168],[50,2610],{"className":2611,"style":2612},[172],"height:0.5944em;vertical-align:-0.15em;",[50,2614,2616],{"className":2615},[182],[50,2617,2619],{"className":2618},[182],[50,2620,2622,2625],{"className":2621},[182],[50,2623,1133],{"className":2624},[182,2262],[50,2626,2628],{"className":2627},[190],[50,2629,2631,2652],{"className":2630},[194,195],[50,2632,2634,2649],{"className":2633},[199],[50,2635,2638],{"className":2636,"style":2637},[203],"height:0.3353em;",[50,2639,2640,2643],{"style":207},[50,2641],{"className":2642,"style":212},[211],[50,2644,2646],{"className":2645},[216,217,218,219],[50,2647,519],{"className":2648},[182,2262,219],[50,2650,227],{"className":2651},[226],[50,2653,2655],{"className":2654},[199],[50,2656,2658],{"className":2657,"style":234},[203],[50,2659],{},[50,2661],{"className":2662,"style":699},[244],[50,2664,1069],{"className":2665},[703],[50,2667],{"className":2668,"style":699},[244],[50,2670,2672,2676,2718,2721,2724],{"className":2671},[168],[50,2673],{"className":2674,"style":2675},[172],"height:0.7778em;vertical-align:-0.1944em;",[50,2677,2679,2682],{"className":2678},[182],[50,2680,95],{"className":2681,"style":2297},[182,2296],[50,2683,2685],{"className":2684},[190],[50,2686,2688,2710],{"className":2687},[194,195],[50,2689,2691,2707],{"className":2690},[199],[50,2692,2695],{"className":2693,"style":2694},[203],"height:0.3361em;",[50,2696,2698,2701],{"style":2697},"top:-2.55em;margin-left:-0.016em;margin-right:0.05em;",[50,2699],{"className":2700,"style":212},[211],[50,2702,2704],{"className":2703},[216,217,218,219],[50,2705,519],{"className":2706},[182,2296,219],[50,2708,227],{"className":2709},[226],[50,2711,2713],{"className":2712},[199],[50,2714,2716],{"className":2715,"style":234},[203],[50,2717],{},[50,2719],{"className":2720,"style":736},[244],[50,2722,2587],{"className":2723},[1212],[50,2725],{"className":2726,"style":736},[244],[50,2728,2730,2734],{"className":2729},[168],[50,2731],{"className":2732,"style":2733},[172],"height:0.9023em;vertical-align:-0.1944em;",[50,2735,2738],{"className":2736},[182,2737],"accent",[50,2739,2741,2812],{"className":2740},[194,195],[50,2742,2744,2809],{"className":2743},[199],[50,2745,2748,2795],{"className":2746,"style":2747},[203],"height:0.7079em;",[50,2749,2751,2755],{"style":2750},"top:-3em;",[50,2752],{"className":2753,"style":2754},[211],"height:3em;",[50,2756,2758,2761],{"className":2757},[182],[50,2759,95],{"className":2760,"style":2297},[182,2296],[50,2762,2764],{"className":2763},[190],[50,2765,2767,2787],{"className":2766},[194,195],[50,2768,2770,2784],{"className":2769},[199],[50,2771,2773],{"className":2772,"style":2694},[203],[50,2774,2775,2778],{"style":2697},[50,2776],{"className":2777,"style":212},[211],[50,2779,2781],{"className":2780},[216,217,218,219],[50,2782,519],{"className":2783},[182,2296,219],[50,2785,227],{"className":2786},[226],[50,2788,2790],{"className":2789},[199],[50,2791,2793],{"className":2792,"style":234},[203],[50,2794],{},[50,2796,2798,2801],{"style":2797},"top:-3.0134em;",[50,2799],{"className":2800,"style":2754},[211],[50,2802,2806],{"className":2803,"style":2805},[2804],"accent-body","left:-0.25em;",[50,2807,2599],{"className":2808},[182],[50,2810,227],{"className":2811},[226],[50,2813,2815],{"className":2814},[199],[50,2816,2819],{"className":2817,"style":2818},[203],"height:0.1944em;",[50,2820],{},[11,2822,2823],{},"Which can also be expressed as:",[50,2825,2827],{"className":2826},[650],[50,2828,2830,2886],{"className":2829},[53],[50,2831,2833],{"className":2832},[57],[59,2834,2835],{"xmlns":61,"display":659},[63,2836,2837,2883],{},[66,2838,2839,2847,2849,2855,2857,2859,2867,2869,2875,2881],{},[80,2840,2841],{},[77,2842,2843,2845],{},[80,2844,1133],{"mathvariant":2240},[80,2846,519],{"mathvariant":2240},[69,2848,1069],{},[77,2850,2851,2853],{},[80,2852,95],{"mathvariant":2279},[80,2854,519],{"mathvariant":2279},[69,2856,2587],{},[69,2858,75],{"stretchy":71},[80,2860,2861],{},[77,2862,2863,2865],{},[80,2864,1074],{"mathvariant":2240},[84,2866,1077],{"mathvariant":2279},[69,2868,1080],{},[77,2870,2871,2873],{},[80,2872,1074],{},[84,2874,86],{},[77,2876,2877,2879],{},[80,2878,82],{},[80,2880,519],{},[69,2882,100],{"stretchy":71},[157,2884,2885],{"encoding":159},"\\boldsymbol{\\epsilon _i} = \\mathbf{y_i} - (\\boldsymbol{\\beta_0} + \\beta_1 x_{i})",[50,2887,2889,2950,3005,3070],{"className":2888,"ariaHidden":89},[164],[50,2890,2892,2895,2941,2944,2947],{"className":2891},[168],[50,2893],{"className":2894,"style":2612},[172],[50,2896,2898],{"className":2897},[182],[50,2899,2901],{"className":2900},[182],[50,2902,2904,2907],{"className":2903},[182],[50,2905,1133],{"className":2906},[182,2262],[50,2908,2910],{"className":2909},[190],[50,2911,2913,2933],{"className":2912},[194,195],[50,2914,2916,2930],{"className":2915},[199],[50,2917,2919],{"className":2918,"style":2637},[203],[50,2920,2921,2924],{"style":207},[50,2922],{"className":2923,"style":212},[211],[50,2925,2927],{"className":2926},[216,217,218,219],[50,2928,519],{"className":2929},[182,2262,219],[50,2931,227],{"className":2932},[226],[50,2934,2936],{"className":2935},[199],[50,2937,2939],{"className":2938,"style":234},[203],[50,2940],{},[50,2942],{"className":2943,"style":699},[244],[50,2945,1069],{"className":2946},[703],[50,2948],{"className":2949,"style":699},[244],[50,2951,2953,2956,2996,2999,3002],{"className":2952},[168],[50,2954],{"className":2955,"style":2675},[172],[50,2957,2959,2962],{"className":2958},[182],[50,2960,95],{"className":2961,"style":2297},[182,2296],[50,2963,2965],{"className":2964},[190],[50,2966,2968,2988],{"className":2967},[194,195],[50,2969,2971,2985],{"className":2970},[199],[50,2972,2974],{"className":2973,"style":2694},[203],[50,2975,2976,2979],{"style":2697},[50,2977],{"className":2978,"style":212},[211],[50,2980,2982],{"className":2981},[216,217,218,219],[50,2983,519],{"className":2984},[182,2296,219],[50,2986,227],{"className":2987},[226],[50,2989,2991],{"className":2990},[199],[50,2992,2994],{"className":2993,"style":234},[203],[50,2995],{},[50,2997],{"className":2998,"style":736},[244],[50,3000,2587],{"className":3001},[1212],[50,3003],{"className":3004,"style":736},[244],[50,3006,3008,3011,3014,3061,3064,3067],{"className":3007},[168],[50,3009],{"className":3010,"style":173},[172],[50,3012,75],{"className":3013},[177],[50,3015,3017],{"className":3016},[182],[50,3018,3020],{"className":3019},[182],[50,3021,3023,3026],{"className":3022},[182],[50,3024,1074],{"className":3025,"style":2263},[182,2262],[50,3027,3029],{"className":3028},[190],[50,3030,3032,3053],{"className":3031},[194,195],[50,3033,3035,3050],{"className":3034},[199],[50,3036,3038],{"className":3037,"style":204},[203],[50,3039,3041,3044],{"style":3040},"top:-2.55em;margin-left:-0.034em;margin-right:0.05em;",[50,3042],{"className":3043,"style":212},[211],[50,3045,3047],{"className":3046},[216,217,218,219],[50,3048,1077],{"className":3049},[182,2296,219],[50,3051,227],{"className":3052},[226],[50,3054,3056],{"className":3055},[199],[50,3057,3059],{"className":3058,"style":234},[203],[50,3060],{},[50,3062],{"className":3063,"style":736},[244],[50,3065,1080],{"className":3066},[1212],[50,3068],{"className":3069,"style":736},[244],[50,3071,3073,3076,3116,3159],{"className":3072},[168],[50,3074],{"className":3075,"style":173},[172],[50,3077,3079,3082],{"className":3078},[182],[50,3080,1074],{"className":3081,"style":1170},[182,186],[50,3083,3085],{"className":3084},[190],[50,3086,3088,3108],{"className":3087},[194,195],[50,3089,3091,3105],{"className":3090},[199],[50,3092,3094],{"className":3093,"style":204},[203],[50,3095,3096,3099],{"style":1185},[50,3097],{"className":3098,"style":212},[211],[50,3100,3102],{"className":3101},[216,217,218,219],[50,3103,86],{"className":3104},[182,219],[50,3106,227],{"className":3107},[226],[50,3109,3111],{"className":3110},[199],[50,3112,3114],{"className":3113,"style":234},[203],[50,3115],{},[50,3117,3119,3122],{"className":3118},[182],[50,3120,82],{"className":3121},[182,186],[50,3123,3125],{"className":3124},[190],[50,3126,3128,3151],{"className":3127},[194,195],[50,3129,3131,3148],{"className":3130},[199],[50,3132,3134],{"className":3133,"style":551},[203],[50,3135,3136,3139],{"style":207},[50,3137],{"className":3138,"style":212},[211],[50,3140,3142],{"className":3141},[216,217,218,219],[50,3143,3145],{"className":3144},[182,219],[50,3146,519],{"className":3147},[182,186,219],[50,3149,227],{"className":3150},[226],[50,3152,3154],{"className":3153},[199],[50,3155,3157],{"className":3156,"style":234},[203],[50,3158],{},[50,3160,100],{"className":3161},[291],[11,3163,3164],{},"To measure the error we can use a loss function. In the case of linear regression, a commonly used loss function is the mean squared error (MSE, for its acronym in English), which is defined as:",[50,3166,3168],{"className":3167},[650],[50,3169,3171,3244],{"className":3170},[53],[50,3172,3174],{"className":3173},[57],[59,3175,3176],{"xmlns":61,"display":659},[63,3177,3178,3241],{},[66,3179,3180,3183,3186,3189,3191,3198,3214,3216,3222,3224,3234],{},[80,3181,3182],{},"M",[80,3184,3185],{},"S",[80,3187,3188],{},"E",[69,3190,1069],{},[3192,3193,3194,3196],"mfrac",{},[84,3195,86],{},[80,3197,142],{},[3199,3200,3201,3204,3212],"munderover",{},[69,3202,3203],{},"∑",[66,3205,3206,3208,3210],{},[80,3207,519],{},[69,3209,1069],{},[84,3211,86],{},[80,3213,142],{},[69,3215,75],{"stretchy":71},[77,3217,3218,3220],{},[80,3219,95],{},[80,3221,519],{},[69,3223,2587],{},[2589,3225,3226,3232],{"accent":89},[77,3227,3228,3230],{},[80,3229,95],{},[80,3231,519],{},[69,3233,2599],{},[3235,3236,3237,3239],"msup",{},[69,3238,100],{"stretchy":71},[84,3240,111],{},[157,3242,3243],{"encoding":159},"MSE = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y_i})^2",[50,3245,3247,3273,3482],{"className":3246,"ariaHidden":89},[164],[50,3248,3250,3253,3257,3261,3264,3267,3270],{"className":3249},[168],[50,3251],{"className":3252,"style":713},[172],[50,3254,3182],{"className":3255,"style":3256},[182,186],"margin-right:0.109em;",[50,3258,3185],{"className":3259,"style":3260},[182,186],"margin-right:0.0576em;",[50,3262,3188],{"className":3263,"style":3260},[182,186],[50,3265],{"className":3266,"style":699},[244],[50,3268,1069],{"className":3269},[703],[50,3271],{"className":3272,"style":699},[244],[50,3274,3276,3280,3350,3353,3430,3433,3473,3476,3479],{"className":3275},[168],[50,3277],{"className":3278,"style":3279},[172],"height:2.9291em;vertical-align:-1.2777em;",[50,3281,3283,3287,3347],{"className":3282},[182],[50,3284],{"className":3285},[177,3286],"nulldelimiter",[50,3288,3290],{"className":3289},[3192],[50,3291,3293,3338],{"className":3292},[194,195],[50,3294,3296,3335],{"className":3295},[199],[50,3297,3300,3312,3323],{"className":3298,"style":3299},[203],"height:1.3214em;",[50,3301,3303,3306],{"style":3302},"top:-2.314em;",[50,3304],{"className":3305,"style":2754},[211],[50,3307,3309],{"className":3308},[182],[50,3310,142],{"className":3311},[182,186],[50,3313,3315,3318],{"style":3314},"top:-3.23em;",[50,3316],{"className":3317,"style":2754},[211],[50,3319],{"className":3320,"style":3322},[3321],"frac-line","border-bottom-width:0.04em;",[50,3324,3326,3329],{"style":3325},"top:-3.677em;",[50,3327],{"className":3328,"style":2754},[211],[50,3330,3332],{"className":3331},[182],[50,3333,86],{"className":3334},[182],[50,3336,227],{"className":3337},[226],[50,3339,3341],{"className":3340},[199],[50,3342,3345],{"className":3343,"style":3344},[203],"height:0.686em;",[50,3346],{},[50,3348],{"className":3349},[291,3286],[50,3351],{"className":3352,"style":245},[244],[50,3354,3358],{"className":3355},[3356,3357],"mop","op-limits",[50,3359,3361,3421],{"className":3360},[194,195],[50,3362,3364,3418],{"className":3363},[199],[50,3365,3368,3390,3403],{"className":3366,"style":3367},[203],"height:1.6514em;",[50,3369,3371,3375],{"style":3370},"top:-1.8723em;margin-left:0em;",[50,3372],{"className":3373,"style":3374},[211],"height:3.05em;",[50,3376,3378],{"className":3377},[216,217,218,219],[50,3379,3381,3384,3387],{"className":3380},[182,219],[50,3382,519],{"className":3383},[182,186,219],[50,3385,1069],{"className":3386},[703,219],[50,3388,86],{"className":3389},[182,219],[50,3391,3393,3396],{"style":3392},"top:-3.05em;",[50,3394],{"className":3395,"style":3374},[211],[50,3397,3398],{},[50,3399,3203],{"className":3400},[3356,3401,3402],"op-symbol","large-op",[50,3404,3406,3409],{"style":3405},"top:-4.3em;margin-left:0em;",[50,3407],{"className":3408,"style":3374},[211],[50,3410,3412],{"className":3411},[216,217,218,219],[50,3413,3415],{"className":3414},[182,219],[50,3416,142],{"className":3417},[182,186,219],[50,3419,227],{"className":3420},[226],[50,3422,3424],{"className":3423},[199],[50,3425,3428],{"className":3426,"style":3427},[203],"height:1.2777em;",[50,3429],{},[50,3431,75],{"className":3432},[177],[50,3434,3436,3439],{"className":3435},[182],[50,3437,95],{"className":3438,"style":252},[182,186],[50,3440,3442],{"className":3441},[190],[50,3443,3445,3465],{"className":3444},[194,195],[50,3446,3448,3462],{"className":3447},[199],[50,3449,3451],{"className":3450,"style":551},[203],[50,3452,3453,3456],{"style":267},[50,3454],{"className":3455,"style":212},[211],[50,3457,3459],{"className":3458},[216,217,218,219],[50,3460,519],{"className":3461},[182,186,219],[50,3463,227],{"className":3464},[226],[50,3466,3468],{"className":3467},[199],[50,3469,3471],{"className":3470,"style":234},[203],[50,3472],{},[50,3474],{"className":3475,"style":736},[244],[50,3477,2587],{"className":3478},[1212],[50,3480],{"className":3481,"style":736},[244],[50,3483,3485,3489,3569],{"className":3484},[168],[50,3486],{"className":3487,"style":3488},[172],"height:1.1141em;vertical-align:-0.25em;",[50,3490,3492],{"className":3491},[182,2737],[50,3493,3495,3561],{"className":3494},[194,195],[50,3496,3498,3558],{"className":3497},[199],[50,3499,3502,3547],{"className":3500,"style":3501},[203],"height:0.6944em;",[50,3503,3504,3507],{"style":2750},[50,3505],{"className":3506,"style":2754},[211],[50,3508,3510,3513],{"className":3509},[182],[50,3511,95],{"className":3512,"style":252},[182,186],[50,3514,3516],{"className":3515},[190],[50,3517,3519,3539],{"className":3518},[194,195],[50,3520,3522,3536],{"className":3521},[199],[50,3523,3525],{"className":3524,"style":551},[203],[50,3526,3527,3530],{"style":267},[50,3528],{"className":3529,"style":212},[211],[50,3531,3533],{"className":3532},[216,217,218,219],[50,3534,519],{"className":3535},[182,186,219],[50,3537,227],{"className":3538},[226],[50,3540,3542],{"className":3541},[199],[50,3543,3545],{"className":3544,"style":234},[203],[50,3546],{},[50,3548,3549,3552],{"style":2750},[50,3550],{"className":3551,"style":2754},[211],[50,3553,3555],{"className":3554,"style":2805},[2804],[50,3556,2599],{"className":3557},[182],[50,3559,227],{"className":3560},[226],[50,3562,3564],{"className":3563},[199],[50,3565,3567],{"className":3566,"style":2818},[203],[50,3568],{},[50,3570,3572,3575],{"className":3571},[291],[50,3573,100],{"className":3574},[291],[50,3576,3578],{"className":3577},[190],[50,3579,3581],{"className":3580},[194],[50,3582,3584],{"className":3583},[199],[50,3585,3588],{"className":3586,"style":3587},[203],"height:0.8641em;",[50,3589,3591,3594],{"style":3590},"top:-3.113em;margin-right:0.05em;",[50,3592],{"className":3593,"style":212},[211],[50,3595,3597],{"className":3596},[216,217,218,219],[50,3598,111],{"className":3599},[182,219],[11,3601,1534],{},[741,3603,3604,3635,3736],{},[744,3605,3606,3634],{},[50,3607,3609,3622],{"className":3608},[53],[50,3610,3612],{"className":3611},[57],[59,3613,3614],{"xmlns":61},[63,3615,3616,3620],{},[66,3617,3618],{},[80,3619,142],{},[157,3621,142],{"encoding":159},[50,3623,3625],{"className":3624,"ariaHidden":89},[164],[50,3626,3628,3631],{"className":3627},[168],[50,3629],{"className":3630,"style":1528},[172],[50,3632,142],{"className":3633},[182,186]," is the number of examples in the dataset.",[744,3636,3637,3706,3707,127],{},[50,3638,3640,3657],{"className":3639},[53],[50,3641,3643],{"className":3642},[57],[59,3644,3645],{"xmlns":61},[63,3646,3647,3655],{},[66,3648,3649],{},[77,3650,3651,3653],{},[80,3652,95],{},[80,3654,519],{},[157,3656,595],{"encoding":159},[50,3658,3660],{"className":3659,"ariaHidden":89},[164],[50,3661,3663,3666],{"className":3662},[168],[50,3664],{"className":3665,"style":605},[172],[50,3667,3669,3672],{"className":3668},[182],[50,3670,95],{"className":3671,"style":252},[182,186],[50,3673,3675],{"className":3674},[190],[50,3676,3678,3698],{"className":3677},[194,195],[50,3679,3681,3695],{"className":3680},[199],[50,3682,3684],{"className":3683,"style":551},[203],[50,3685,3686,3689],{"style":267},[50,3687],{"className":3688,"style":212},[211],[50,3690,3692],{"className":3691},[216,217,218,219],[50,3693,519],{"className":3694},[182,186,219],[50,3696,227],{"className":3697},[226],[50,3699,3701],{"className":3700},[199],[50,3702,3704],{"className":3703,"style":234},[203],[50,3705],{}," is the actual value of the dependent variable for example ",[50,3708,3710,3723],{"className":3709},[53],[50,3711,3713],{"className":3712},[57],[59,3714,3715],{"xmlns":61},[63,3716,3717,3721],{},[66,3718,3719],{},[80,3720,519],{},[157,3722,519],{"encoding":159},[50,3724,3726],{"className":3725,"ariaHidden":89},[164],[50,3727,3729,3733],{"className":3728},[168],[50,3730],{"className":3731,"style":3732},[172],"height:0.6595em;",[50,3734,519],{"className":3735},[182,186],[744,3737,3738,3852,3853,127],{},[50,3739,3741,3763],{"className":3740},[53],[50,3742,3744],{"className":3743},[57],[59,3745,3746],{"xmlns":61},[63,3747,3748,3760],{},[66,3749,3750],{},[77,3751,3752,3758],{},[2589,3753,3754,3756],{"accent":89},[80,3755,95],{},[69,3757,2599],{},[80,3759,519],{},[157,3761,3762],{"encoding":159},"\\hat{y}_i",[50,3764,3766],{"className":3765,"ariaHidden":89},[164],[50,3767,3769,3772],{"className":3768},[168],[50,3770],{"className":3771,"style":691},[172],[50,3773,3775,3818],{"className":3774},[182],[50,3776,3778],{"className":3777},[182,2737],[50,3779,3781,3810],{"className":3780},[194,195],[50,3782,3784,3807],{"className":3783},[199],[50,3785,3787,3795],{"className":3786,"style":3501},[203],[50,3788,3789,3792],{"style":2750},[50,3790],{"className":3791,"style":2754},[211],[50,3793,95],{"className":3794,"style":252},[182,186],[50,3796,3797,3800],{"style":2750},[50,3798],{"className":3799,"style":2754},[211],[50,3801,3804],{"className":3802,"style":3803},[2804],"left:-0.1944em;",[50,3805,2599],{"className":3806},[182],[50,3808,227],{"className":3809},[226],[50,3811,3813],{"className":3812},[199],[50,3814,3816],{"className":3815,"style":2818},[203],[50,3817],{},[50,3819,3821],{"className":3820},[190],[50,3822,3824,3844],{"className":3823},[194,195],[50,3825,3827,3841],{"className":3826},[199],[50,3828,3830],{"className":3829,"style":551},[203],[50,3831,3832,3835],{"style":267},[50,3833],{"className":3834,"style":212},[211],[50,3836,3838],{"className":3837},[216,217,218,219],[50,3839,519],{"className":3840},[182,186,219],[50,3842,227],{"className":3843},[226],[50,3845,3847],{"className":3846},[199],[50,3848,3850],{"className":3849,"style":234},[203],[50,3851],{}," is the model's prediction for example ",[50,3854,3856,3869],{"className":3855},[53],[50,3857,3859],{"className":3858},[57],[59,3860,3861],{"xmlns":61},[63,3862,3863,3867],{},[66,3864,3865],{},[80,3866,519],{},[157,3868,519],{"encoding":159},[50,3870,3872],{"className":3871,"ariaHidden":89},[164],[50,3873,3875,3878],{"className":3874},[168],[50,3876],{"className":3877,"style":3732},[172],[50,3879,519],{"className":3880},[182,186],[11,3882,3883],{},"Minimizing the MSE is equivalent to minimizing the sum of squared errors:",[50,3885,3887],{"className":3886},[650],[50,3888,3890,3942],{"className":3889},[53],[50,3891,3893],{"className":3892},[57],[59,3894,3895],{"xmlns":61,"display":659},[63,3896,3897,3939],{},[66,3898,3899,3913,3915,3921,3923,3933],{},[3199,3900,3901,3903,3911],{},[69,3902,3203],{},[66,3904,3905,3907,3909],{},[80,3906,519],{},[69,3908,1069],{},[84,3910,86],{},[80,3912,142],{},[69,3914,75],{"stretchy":71},[77,3916,3917,3919],{},[80,3918,95],{},[80,3920,519],{},[69,3922,2587],{},[2589,3924,3925,3931],{"accent":89},[77,3926,3927,3929],{},[80,3928,95],{},[80,3930,519],{},[69,3932,2599],{},[3235,3934,3935,3937],{},[69,3936,100],{"stretchy":71},[84,3938,111],{},[157,3940,3941],{"encoding":159},"\\sum_{i=1}^{n} (y_i - \\hat{y_i})^2",[50,3943,3945,4070],{"className":3944,"ariaHidden":89},[164],[50,3946,3948,3951,4018,4021,4061,4064,4067],{"className":3947},[168],[50,3949],{"className":3950,"style":3279},[172],[50,3952,3954],{"className":3953},[3356,3357],[50,3955,3957,4010],{"className":3956},[194,195],[50,3958,3960,4007],{"className":3959},[199],[50,3961,3963,3983,3993],{"className":3962,"style":3367},[203],[50,3964,3965,3968],{"style":3370},[50,3966],{"className":3967,"style":3374},[211],[50,3969,3971],{"className":3970},[216,217,218,219],[50,3972,3974,3977,3980],{"className":3973},[182,219],[50,3975,519],{"className":3976},[182,186,219],[50,3978,1069],{"className":3979},[703,219],[50,3981,86],{"className":3982},[182,219],[50,3984,3985,3988],{"style":3392},[50,3986],{"className":3987,"style":3374},[211],[50,3989,3990],{},[50,3991,3203],{"className":3992},[3356,3401,3402],[50,3994,3995,3998],{"style":3405},[50,3996],{"className":3997,"style":3374},[211],[50,3999,4001],{"className":4000},[216,217,218,219],[50,4002,4004],{"className":4003},[182,219],[50,4005,142],{"className":4006},[182,186,219],[50,4008,227],{"className":4009},[226],[50,4011,4013],{"className":4012},[199],[50,4014,4016],{"className":4015,"style":3427},[203],[50,4017],{},[50,4019,75],{"className":4020},[177],[50,4022,4024,4027],{"className":4023},[182],[50,4025,95],{"className":4026,"style":252},[182,186],[50,4028,4030],{"className":4029},[190],[50,4031,4033,4053],{"className":4032},[194,195],[50,4034,4036,4050],{"className":4035},[199],[50,4037,4039],{"className":4038,"style":551},[203],[50,4040,4041,4044],{"style":267},[50,4042],{"className":4043,"style":212},[211],[50,4045,4047],{"className":4046},[216,217,218,219],[50,4048,519],{"className":4049},[182,186,219],[50,4051,227],{"className":4052},[226],[50,4054,4056],{"className":4055},[199],[50,4057,4059],{"className":4058,"style":234},[203],[50,4060],{},[50,4062],{"className":4063,"style":736},[244],[50,4065,2587],{"className":4066},[1212],[50,4068],{"className":4069,"style":736},[244],[50,4071,4073,4076,4155],{"className":4072},[168],[50,4074],{"className":4075,"style":3488},[172],[50,4077,4079],{"className":4078},[182,2737],[50,4080,4082,4147],{"className":4081},[194,195],[50,4083,4085,4144],{"className":4084},[199],[50,4086,4088,4133],{"className":4087,"style":3501},[203],[50,4089,4090,4093],{"style":2750},[50,4091],{"className":4092,"style":2754},[211],[50,4094,4096,4099],{"className":4095},[182],[50,4097,95],{"className":4098,"style":252},[182,186],[50,4100,4102],{"className":4101},[190],[50,4103,4105,4125],{"className":4104},[194,195],[50,4106,4108,4122],{"className":4107},[199],[50,4109,4111],{"className":4110,"style":551},[203],[50,4112,4113,4116],{"style":267},[50,4114],{"className":4115,"style":212},[211],[50,4117,4119],{"className":4118},[216,217,218,219],[50,4120,519],{"className":4121},[182,186,219],[50,4123,227],{"className":4124},[226],[50,4126,4128],{"className":4127},[199],[50,4129,4131],{"className":4130,"style":234},[203],[50,4132],{},[50,4134,4135,4138],{"style":2750},[50,4136],{"className":4137,"style":2754},[211],[50,4139,4141],{"className":4140,"style":2805},[2804],[50,4142,2599],{"className":4143},[182],[50,4145,227],{"className":4146},[226],[50,4148,4150],{"className":4149},[199],[50,4151,4153],{"className":4152,"style":2818},[203],[50,4154],{},[50,4156,4158,4161],{"className":4157},[291],[50,4159,100],{"className":4160},[291],[50,4162,4164],{"className":4163},[190],[50,4165,4167],{"className":4166},[194],[50,4168,4170],{"className":4169},[199],[50,4171,4173],{"className":4172,"style":3587},[203],[50,4174,4175,4178],{"style":3590},[50,4176],{"className":4177,"style":212},[211],[50,4179,4181],{"className":4180},[216,217,218,219],[50,4182,111],{"className":4183},[182,219],[11,4185,4186],{},"Or also:",[50,4188,4190],{"className":4189},[650],[50,4191,4193,4259],{"className":4192},[53],[50,4194,4196],{"className":4195},[57],[59,4197,4198],{"xmlns":61,"display":659},[63,4199,4200,4256],{},[66,4201,4202,4216,4218,4224,4226,4228,4234,4236,4242,4248,4250],{},[3199,4203,4204,4206,4214],{},[69,4205,3203],{},[66,4207,4208,4210,4212],{},[80,4209,519],{},[69,4211,1069],{},[84,4213,86],{},[80,4215,142],{},[69,4217,75],{"stretchy":71},[77,4219,4220,4222],{},[80,4221,95],{},[80,4223,519],{},[69,4225,2587],{},[69,4227,75],{"stretchy":71},[77,4229,4230,4232],{},[80,4231,1074],{},[84,4233,1077],{},[69,4235,1080],{},[77,4237,4238,4240],{},[80,4239,1074],{},[84,4241,86],{},[77,4243,4244,4246],{},[80,4245,82],{},[80,4247,519],{},[69,4249,100],{"stretchy":71},[3235,4251,4252,4254],{},[69,4253,100],{"stretchy":71},[84,4255,111],{},[157,4257,4258],{"encoding":159},"\\sum_{i=1}^{n} (y_i - (\\beta_0 + \\beta_1 x_i))^2",[50,4260,4262,4387,4445],{"className":4261,"ariaHidden":89},[164],[50,4263,4265,4268,4335,4338,4378,4381,4384],{"className":4264},[168],[50,4266],{"className":4267,"style":3279},[172],[50,4269,4271],{"className":4270},[3356,3357],[50,4272,4274,4327],{"className":4273},[194,195],[50,4275,4277,4324],{"className":4276},[199],[50,4278,4280,4300,4310],{"className":4279,"style":3367},[203],[50,4281,4282,4285],{"style":3370},[50,4283],{"className":4284,"style":3374},[211],[50,4286,4288],{"className":4287},[216,217,218,219],[50,4289,4291,4294,4297],{"className":4290},[182,219],[50,4292,519],{"className":4293},[182,186,219],[50,4295,1069],{"className":4296},[703,219],[50,4298,86],{"className":4299},[182,219],[50,4301,4302,4305],{"style":3392},[50,4303],{"className":4304,"style":3374},[211],[50,4306,4307],{},[50,4308,3203],{"className":4309},[3356,3401,3402],[50,4311,4312,4315],{"style":3405},[50,4313],{"className":4314,"style":3374},[211],[50,4316,4318],{"className":4317},[216,217,218,219],[50,4319,4321],{"className":4320},[182,219],[50,4322,142],{"className":4323},[182,186,219],[50,4325,227],{"className":4326},[226],[50,4328,4330],{"className":4329},[199],[50,4331,4333],{"className":4332,"style":3427},[203],[50,4334],{},[50,4336,75],{"className":4337},[177],[50,4339,4341,4344],{"className":4340},[182],[50,4342,95],{"className":4343,"style":252},[182,186],[50,4345,4347],{"className":4346},[190],[50,4348,4350,4370],{"className":4349},[194,195],[50,4351,4353,4367],{"className":4352},[199],[50,4354,4356],{"className":4355,"style":551},[203],[50,4357,4358,4361],{"style":267},[50,4359],{"className":4360,"style":212},[211],[50,4362,4364],{"className":4363},[216,217,218,219],[50,4365,519],{"className":4366},[182,186,219],[50,4368,227],{"className":4369},[226],[50,4371,4373],{"className":4372},[199],[50,4374,4376],{"className":4375,"style":234},[203],[50,4377],{},[50,4379],{"className":4380,"style":736},[244],[50,4382,2587],{"className":4383},[1212],[50,4385],{"className":4386,"style":736},[244],[50,4388,4390,4393,4396,4436,4439,4442],{"className":4389},[168],[50,4391],{"className":4392,"style":173},[172],[50,4394,75],{"className":4395},[177],[50,4397,4399,4402],{"className":4398},[182],[50,4400,1074],{"className":4401,"style":1170},[182,186],[50,4403,4405],{"className":4404},[190],[50,4406,4408,4428],{"className":4407},[194,195],[50,4409,4411,4425],{"className":4410},[199],[50,4412,4414],{"className":4413,"style":204},[203],[50,4415,4416,4419],{"style":1185},[50,4417],{"className":4418,"style":212},[211],[50,4420,4422],{"className":4421},[216,217,218,219],[50,4423,1077],{"className":4424},[182,219],[50,4426,227],{"className":4427},[226],[50,4429,4431],{"className":4430},[199],[50,4432,4434],{"className":4433,"style":234},[203],[50,4435],{},[50,4437],{"className":4438,"style":736},[244],[50,4440,1080],{"className":4441},[1212],[50,4443],{"className":4444,"style":736},[244],[50,4446,4448,4451,4491,4531,4534],{"className":4447},[168],[50,4449],{"className":4450,"style":3488},[172],[50,4452,4454,4457],{"className":4453},[182],[50,4455,1074],{"className":4456,"style":1170},[182,186],[50,4458,4460],{"className":4459},[190],[50,4461,4463,4483],{"className":4462},[194,195],[50,4464,4466,4480],{"className":4465},[199],[50,4467,4469],{"className":4468,"style":204},[203],[50,4470,4471,4474],{"style":1185},[50,4472],{"className":4473,"style":212},[211],[50,4475,4477],{"className":4476},[216,217,218,219],[50,4478,86],{"className":4479},[182,219],[50,4481,227],{"className":4482},[226],[50,4484,4486],{"className":4485},[199],[50,4487,4489],{"className":4488,"style":234},[203],[50,4490],{},[50,4492,4494,4497],{"className":4493},[182],[50,4495,82],{"className":4496},[182,186],[50,4498,4500],{"className":4499},[190],[50,4501,4503,4523],{"className":4502},[194,195],[50,4504,4506,4520],{"className":4505},[199],[50,4507,4509],{"className":4508,"style":551},[203],[50,4510,4511,4514],{"style":207},[50,4512],{"className":4513,"style":212},[211],[50,4515,4517],{"className":4516},[216,217,218,219],[50,4518,519],{"className":4519},[182,186,219],[50,4521,227],{"className":4522},[226],[50,4524,4526],{"className":4525},[199],[50,4527,4529],{"className":4528,"style":234},[203],[50,4530],{},[50,4532,100],{"className":4533},[291],[50,4535,4537,4540],{"className":4536},[291],[50,4538,100],{"className":4539},[291],[50,4541,4543],{"className":4542},[190],[50,4544,4546],{"className":4545},[194],[50,4547,4549],{"className":4548},[199],[50,4550,4552],{"className":4551,"style":3587},[203],[50,4553,4554,4557],{"style":3590},[50,4555],{"className":4556,"style":212},[211],[50,4558,4560],{"className":4559},[216,217,218,219],[50,4561,111],{"className":4562},[182,219],[11,4564,4565,4566,4569,4570,4604],{},"This is known as the ",[747,4567,4568],{},"least squares method",", and it is a commonly used technique to find the coefficients ",[50,4571,4573,4586],{"className":4572},[53],[50,4574,4576],{"className":4575},[57],[59,4577,4578],{"xmlns":61},[63,4579,4580,4584],{},[66,4581,4582],{},[80,4583,1074],{"mathvariant":2240},[157,4585,2243],{"encoding":159},[50,4587,4589],{"className":4588,"ariaHidden":89},[164],[50,4590,4592,4595],{"className":4591},[168],[50,4593],{"className":4594,"style":691},[172],[50,4596,4598],{"className":4597},[182],[50,4599,4601],{"className":4600},[182],[50,4602,1074],{"className":4603,"style":2263},[182,2262]," that best fit the data.",[4606,4607,4608],"blockquote",{},[11,4609,4610],{},"Why are squares used? Because by squaring the differences, larger errors are penalized more, which helps to find a better fitting line for the data.",[11,4612,4613,4614,4648,4649,4718,4719,4789],{},"Let's now see how we obtain those coefficients ",[50,4615,4617,4630],{"className":4616},[53],[50,4618,4620],{"className":4619},[57],[59,4621,4622],{"xmlns":61},[63,4623,4624,4628],{},[66,4625,4626],{},[80,4627,1074],{"mathvariant":2240},[157,4629,2243],{"encoding":159},[50,4631,4633],{"className":4632,"ariaHidden":89},[164],[50,4634,4636,4639],{"className":4635},[168],[50,4637],{"className":4638,"style":691},[172],[50,4640,4642],{"className":4641},[182],[50,4643,4645],{"className":4644},[182],[50,4646,1074],{"className":4647,"style":2263},[182,2262]," using the least squares method. To find the values of ",[50,4650,4652,4669],{"className":4651},[53],[50,4653,4655],{"className":4654},[57],[59,4656,4657],{"xmlns":61},[63,4658,4659,4667],{},[66,4660,4661],{},[77,4662,4663,4665],{},[80,4664,1074],{},[84,4666,1077],{},[157,4668,1789],{"encoding":159},[50,4670,4672],{"className":4671,"ariaHidden":89},[164],[50,4673,4675,4678],{"className":4674},[168],[50,4676],{"className":4677,"style":691},[172],[50,4679,4681,4684],{"className":4680},[182],[50,4682,1074],{"className":4683,"style":1170},[182,186],[50,4685,4687],{"className":4686},[190],[50,4688,4690,4710],{"className":4689},[194,195],[50,4691,4693,4707],{"className":4692},[199],[50,4694,4696],{"className":4695,"style":204},[203],[50,4697,4698,4701],{"style":1185},[50,4699],{"className":4700,"style":212},[211],[50,4702,4704],{"className":4703},[216,217,218,219],[50,4705,1077],{"className":4706},[182,219],[50,4708,227],{"className":4709},[226],[50,4711,4713],{"className":4712},[199],[50,4714,4716],{"className":4715,"style":234},[203],[50,4717],{}," and ",[50,4720,4722,4740],{"className":4721},[53],[50,4723,4725],{"className":4724},[57],[59,4726,4727],{"xmlns":61},[63,4728,4729,4737],{},[66,4730,4731],{},[77,4732,4733,4735],{},[80,4734,1074],{},[84,4736,86],{},[157,4738,4739],{"encoding":159},"\\beta_1",[50,4741,4743],{"className":4742,"ariaHidden":89},[164],[50,4744,4746,4749],{"className":4745},[168],[50,4747],{"className":4748,"style":691},[172],[50,4750,4752,4755],{"className":4751},[182],[50,4753,1074],{"className":4754,"style":1170},[182,186],[50,4756,4758],{"className":4757},[190],[50,4759,4761,4781],{"className":4760},[194,195],[50,4762,4764,4778],{"className":4763},[199],[50,4765,4767],{"className":4766,"style":204},[203],[50,4768,4769,4772],{"style":1185},[50,4770],{"className":4771,"style":212},[211],[50,4773,4775],{"className":4774},[216,217,218,219],[50,4776,86],{"className":4777},[182,219],[50,4779,227],{"className":4780},[226],[50,4782,4784],{"className":4783},[199],[50,4785,4787],{"className":4786,"style":234},[203],[50,4788],{},", we can use the following formulas, which are obtained by taking the derivative of the MSE loss function with respect to the coefficients and setting the derivatives equal to zero:",[50,4791,4793],{"className":4792},[650],[50,4794,4796,4895],{"className":4795},[53],[50,4797,4799],{"className":4798},[57],[59,4800,4801],{"xmlns":61,"display":659},[63,4802,4803,4892],{},[66,4804,4805,4811,4813],{},[77,4806,4807,4809],{},[80,4808,1074],{},[84,4810,86],{},[69,4812,1069],{},[3192,4814,4815,4855],{},[66,4816,4817,4819,4821,4823,4829,4835,4837,4839,4841,4847,4849],{},[80,4818,142],{},[69,4820,3203],{},[69,4822,75],{"stretchy":71},[77,4824,4825,4827],{},[80,4826,82],{},[80,4828,519],{},[77,4830,4831,4833],{},[80,4832,95],{},[80,4834,519],{},[69,4836,100],{"stretchy":71},[69,4838,2587],{},[69,4840,3203],{},[77,4842,4843,4845],{},[80,4844,82],{},[80,4846,519],{},[69,4848,3203],{},[77,4850,4851,4853],{},[80,4852,95],{},[80,4854,519],{},[66,4856,4857,4859,4861,4863,4872,4874,4876,4878,4880,4886],{},[80,4858,142],{},[69,4860,3203],{},[69,4862,75],{"stretchy":71},[4864,4865,4866,4868,4870],"msubsup",{},[80,4867,82],{},[80,4869,519],{},[84,4871,111],{},[69,4873,100],{"stretchy":71},[69,4875,2587],{},[69,4877,75],{"stretchy":71},[69,4879,3203],{},[77,4881,4882,4884],{},[80,4883,82],{},[80,4885,519],{},[3235,4887,4888,4890],{},[69,4889,100],{"stretchy":71},[84,4891,111],{},[157,4893,4894],{"encoding":159},"\\beta_1 = \\frac{n \\sum (x_i y_i) - \\sum x_i \\sum y_i}{n \\sum (x_i^2) - (\\sum 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= \\bar{y} - \\beta_1 \\bar{x}",[50,5426,5428,5483,5541],{"className":5427,"ariaHidden":89},[164],[50,5429,5431,5434,5474,5477,5480],{"className":5430},[168],[50,5432],{"className":5433,"style":691},[172],[50,5435,5437,5440],{"className":5436},[182],[50,5438,1074],{"className":5439,"style":1170},[182,186],[50,5441,5443],{"className":5442},[190],[50,5444,5446,5466],{"className":5445},[194,195],[50,5447,5449,5463],{"className":5448},[199],[50,5450,5452],{"className":5451,"style":204},[203],[50,5453,5454,5457],{"style":1185},[50,5455],{"className":5456,"style":212},[211],[50,5458,5460],{"className":5459},[216,217,218,219],[50,5461,1077],{"className":5462},[182,219],[50,5464,227],{"className":5465},[226],[50,5467,5469],{"className":5468},[199],[50,5470,5472],{"className":5471,"style":234},[203],[50,5473],{},[50,5475],{"className":5476,"style":699},[244],[50,5478,1069],{"className":5479},[703],[50,5481],{"className":5482,"style":699},[244],[50,5484,5486,5489,5532,5535,5538],{"className":5485},[168],[50,5487],{"className":5488,"style":2675},[172],[50,5490,5492],{"className":5491},[182,2737],[50,5493,5495,5524],{"className":5494},[194,195],[50,5496,5498,5521],{"className":5497},[199],[50,5499,5502,5510],{"className":5500,"style":5501},[203],"height:0.5678em;",[50,5503,5504,5507],{"style":2750},[50,5505],{"className":5506,"style":2754},[211],[50,5508,95],{"className":5509,"style":252},[182,186],[50,5511,5512,5515],{"style":2750},[50,5513],{"className":5514,"style":2754},[211],[50,5516,5518],{"className":5517,"style":3803},[2804],[50,5519,5407],{"className":5520},[182],[50,5522,227],{"className":5523},[226],[50,5525,5527],{"className":5526},[199],[50,5528,5530],{"className":5529,"style":2818},[203],[50,5531],{},[50,5533],{"className":5534,"style":736},[244],[50,5536,2587],{"className":5537},[1212],[50,5539],{"className":5540,"style":736},[244],[50,5542,5544,5547,5587],{"className":5543},[168],[50,5545],{"className":5546,"style":691},[172],[50,5548,5550,5553],{"className":5549},[182],[50,5551,1074],{"className":5552,"style":1170},[182,186],[50,5554,5556],{"className":5555},[190],[50,5557,5559,5579],{"className":5558},[194,195],[50,5560,5562,5576],{"className":5561},[199],[50,5563,5565],{"className":5564,"style":204},[203],[50,5566,5567,5570],{"style":1185},[50,5568],{"className":5569,"style":212},[211],[50,5571,5573],{"className":5572},[216,217,218,219],[50,5574,86],{"className":5575},[182,219],[50,5577,227],{"className":5578},[226],[50,5580,5582],{"className":5581},[199],[50,5583,5585],{"className":5584,"style":234},[203],[50,5586],{},[50,5588,5590],{"className":5589},[182,2737],[50,5591,5593],{"className":5592},[194],[50,5594,5596],{"className":5595},[199],[50,5597,5599,5607],{"className":5598,"style":5501},[203],[50,5600,5601,5604],{"style":2750},[50,5602],{"className":5603,"style":2754},[211],[50,5605,82],{"className":5606},[182,186],[50,5608,5609,5612],{"style":2750},[50,5610],{"className":5611,"style":2754},[211],[50,5613,5616],{"className":5614,"style":5615},[2804],"left:-0.2222em;",[50,5617,5407],{"className":5618},[182],[11,5620,1534],{},[741,5622,5623,5653,5794],{},[744,5624,5625,3634],{},[50,5626,5628,5641],{"className":5627},[53],[50,5629,5631],{"className":5630},[57],[59,5632,5633],{"xmlns":61},[63,5634,5635,5639],{},[66,5636,5637],{},[80,5638,142],{},[157,5640,142],{"encoding":159},[50,5642,5644],{"className":5643,"ariaHidden":89},[164],[50,5645,5647,5650],{"className":5646},[168],[50,5648],{"className":5649,"style":1528},[172],[50,5651,142],{"className":5652},[182,186],[744,5654,5655,4718,5724,5793],{},[50,5656,5658,5675],{"className":5657},[53],[50,5659,5661],{"className":5660},[57],[59,5662,5663],{"xmlns":61},[63,5664,5665,5673],{},[66,5666,5667],{},[77,5668,5669,5671],{},[80,5670,82],{},[80,5672,519],{},[157,5674,522],{"encoding":159},[50,5676,5678],{"className":5677,"ariaHidden":89},[164],[50,5679,5681,5684],{"className":5680},[168],[50,5682],{"className":5683,"style":532},[172],[50,5685,5687,5690],{"className":5686},[182],[50,5688,82],{"className":5689},[182,186],[50,5691,5693],{"className":5692},[190],[50,5694,5696,5716],{"className":5695},[194,195],[50,5697,5699,5713],{"className":5698},[199],[50,5700,5702],{"className":5701,"style":551},[203],[50,5703,5704,5707],{"style":207},[50,5705],{"className":5706,"style":212},[211],[50,5708,5710],{"className":5709},[216,217,218,219],[50,5711,519],{"className":5712},[182,186,219],[50,5714,227],{"className":5715},[226],[50,5717,5719],{"className":5718},[199],[50,5720,5722],{"className":5721,"style":234},[203],[50,5723],{},[50,5725,5727,5744],{"className":5726},[53],[50,5728,5730],{"className":5729},[57],[59,5731,5732],{"xmlns":61},[63,5733,5734,5742],{},[66,5735,5736],{},[77,5737,5738,5740],{},[80,5739,95],{},[80,5741,519],{},[157,5743,595],{"encoding":159},[50,5745,5747],{"className":5746,"ariaHidden":89},[164],[50,5748,5750,5753],{"className":5749},[168],[50,5751],{"className":5752,"style":605},[172],[50,5754,5756,5759],{"className":5755},[182],[50,5757,95],{"className":5758,"style":252},[182,186],[50,5760,5762],{"className":5761},[190],[50,5763,5765,5785],{"className":5764},[194,195],[50,5766,5768,5782],{"className":5767},[199],[50,5769,5771],{"className":5770,"style":551},[203],[50,5772,5773,5776],{"style":267},[50,5774],{"className":5775,"style":212},[211],[50,5777,5779],{"className":5778},[216,217,218,219],[50,5780,519],{"className":5781},[182,186,219],[50,5783,227],{"className":5784},[226],[50,5786,5788],{"className":5787},[199],[50,5789,5791],{"className":5790,"style":234},[203],[50,5792],{}," are the values of the independent and dependent variables for each example.",[744,5795,5796,4718,5857,5930],{},[50,5797,5799,5817],{"className":5798},[53],[50,5800,5802],{"className":5801},[57],[59,5803,5804],{"xmlns":61},[63,5805,5806,5814],{},[66,5807,5808],{},[2589,5809,5810,5812],{"accent":89},[80,5811,82],{},[69,5813,5407],{},[157,5815,5816],{"encoding":159},"\\bar{x}",[50,5818,5820],{"className":5819,"ariaHidden":89},[164],[50,5821,5823,5826],{"className":5822},[168],[50,5824],{"className":5825,"style":5501},[172],[50,5827,5829],{"className":5828},[182,2737],[50,5830,5832],{"className":5831},[194],[50,5833,5835],{"className":5834},[199],[50,5836,5838,5846],{"className":5837,"style":5501},[203],[50,5839,5840,5843],{"style":2750},[50,5841],{"className":5842,"style":2754},[211],[50,5844,82],{"className":5845},[182,186],[50,5847,5848,5851],{"style":2750},[50,5849],{"className":5850,"style":2754},[211],[50,5852,5854],{"className":5853,"style":5615},[2804],[50,5855,5407],{"className":5856},[182],[50,5858,5860,5878],{"className":5859},[53],[50,5861,5863],{"className":5862},[57],[59,5864,5865],{"xmlns":61},[63,5866,5867,5875],{},[66,5868,5869],{},[2589,5870,5871,5873],{"accent":89},[80,5872,95],{},[69,5874,5407],{},[157,5876,5877],{"encoding":159},"\\bar{y}",[50,5879,5881],{"className":5880,"ariaHidden":89},[164],[50,5882,5884,5888],{"className":5883},[168],[50,5885],{"className":5886,"style":5887},[172],"height:0.7622em;vertical-align:-0.1944em;",[50,5889,5891],{"className":5890},[182,2737],[50,5892,5894,5922],{"className":5893},[194,195],[50,5895,5897,5919],{"className":5896},[199],[50,5898,5900,5908],{"className":5899,"style":5501},[203],[50,5901,5902,5905],{"style":2750},[50,5903],{"className":5904,"style":2754},[211],[50,5906,95],{"className":5907,"style":252},[182,186],[50,5909,5910,5913],{"style":2750},[50,5911],{"className":5912,"style":2754},[211],[50,5914,5916],{"className":5915,"style":3803},[2804],[50,5917,5407],{"className":5918},[182],[50,5920,227],{"className":5921},[226],[50,5923,5925],{"className":5924},[199],[50,5926,5928],{"className":5927,"style":2818},[203],[50,5929],{}," are the means (or averages) of the independent and dependent variables, respectively.",[11,5932,5933],{},"Let's do an example to understand how to apply it. Suppose we have a dataset with the following characteristics:",[5935,5936,5937,5950],"table",{},[5938,5939,5940],"thead",{},[5941,5942,5943,5947],"tr",{},[5944,5945,5946],"th",{},"Size (m²)",[5944,5948,5949],{},"Price (USD)",[5951,5952,5953,5962,5970],"tbody",{},[5941,5954,5955,5959],{},[5956,5957,5958],"td",{},"50",[5956,5960,5961],{},"100,000",[5941,5963,5964,5967],{},[5956,5965,5966],{},"100",[5956,5968,5969],{},"200,000",[5941,5971,5972,5975],{},[5956,5973,5974],{},"150",[5956,5976,5977],{},"300,000",[11,5979,5980,5981,5984],{},"We want to build a linear regression model to ",[747,5982,5983],{},"predict the price"," of a house based on its size. In this case, the independent variable is the size (X) and the dependent variable is the price (Y). The linear regression model can be expressed as:",[50,5986,5988],{"className":5987},[650],[50,5989,5991,6027],{"className":5990},[53],[50,5992,5994],{"className":5993},[57],[59,5995,5996],{"xmlns":61,"display":659},[63,5997,5998,6024],{},[66,5999,6000,6002,6004,6010,6012,6018,6020,6022],{},[80,6001,95],{},[69,6003,1069],{},[77,6005,6006,6008],{},[80,6007,1074],{},[84,6009,1077],{},[69,6011,1080],{},[77,6013,6014,6016],{},[80,6015,1074],{},[84,6017,86],{},[80,6019,82],{},[69,6021,1080],{},[80,6023,1133],{},[157,6025,6026],{"encoding":159},"y = \\beta_0 + \\beta_1 x + \\epsilon",[50,6028,6030,6048,6103,6161],{"className":6029,"ariaHidden":89},[164],[50,6031,6033,6036,6039,6042,6045],{"className":6032},[168],[50,6034],{"className":6035,"style":605},[172],[50,6037,95],{"className":6038,"style":252},[182,186],[50,6040],{"className":6041,"style":699},[244],[50,6043,1069],{"className":6044},[703],[50,6046],{"className":6047,"style":699},[244],[50,6049,6051,6054,6094,6097,6100],{"className":6050},[168],[50,6052],{"className":6053,"style":691},[172],[50,6055,6057,6060],{"className":6056},[182],[50,6058,1074],{"className":6059,"style":1170},[182,186],[50,6061,6063],{"className":6062},[190],[50,6064,6066,6086],{"className":6065},[194,195],[50,6067,6069,6083],{"className":6068},[199],[50,6070,6072],{"className":6071,"style":204},[203],[50,6073,6074,6077],{"style":1185},[50,6075],{"className":6076,"style":212},[211],[50,6078,6080],{"className":6079},[216,217,218,219],[50,6081,1077],{"className":6082},[182,219],[50,6084,227],{"className":6085},[226],[50,6087,6089],{"className":6088},[199],[50,6090,6092],{"className":6091,"style":234},[203],[50,6093],{},[50,6095],{"className":6096,"style":736},[244],[50,6098,1080],{"className":6099},[1212],[50,6101],{"className":6102,"style":736},[244],[50,6104,6106,6109,6149,6152,6155,6158],{"className":6105},[168],[50,6107],{"className":6108,"style":691},[172],[50,6110,6112,6115],{"className":6111},[182],[50,6113,1074],{"className":6114,"style":1170},[182,186],[50,6116,6118],{"className":6117},[190],[50,6119,6121,6141],{"className":6120},[194,195],[50,6122,6124,6138],{"className":6123},[199],[50,6125,6127],{"className":6126,"style":204},[203],[50,6128,6129,6132],{"style":1185},[50,6130],{"className":6131,"style":212},[211],[50,6133,6135],{"className":6134},[216,217,218,219],[50,6136,86],{"className":6137},[182,219],[50,6139,227],{"className":6140},[226],[50,6142,6144],{"className":6143},[199],[50,6145,6147],{"className":6146,"style":234},[203],[50,6148],{},[50,6150,82],{"className":6151},[182,186],[50,6153],{"className":6154,"style":736},[244],[50,6156,1080],{"className":6157},[1212],[50,6159],{"className":6160,"style":736},[244],[50,6162,6164,6167],{"className":6163},[168],[50,6165],{"className":6166,"style":1528},[172],[50,6168,1133],{"className":6169},[182,186],[11,6171,1534],{},[741,6173,6174,6205,6236,6308,6380],{},[744,6175,6176,6204],{},[50,6177,6179,6192],{"className":6178},[53],[50,6180,6182],{"className":6181},[57],[59,6183,6184],{"xmlns":61},[63,6185,6186,6190],{},[66,6187,6188],{},[80,6189,95],{},[157,6191,95],{"encoding":159},[50,6193,6195],{"className":6194,"ariaHidden":89},[164],[50,6196,6198,6201],{"className":6197},[168],[50,6199],{"className":6200,"style":605},[172],[50,6202,95],{"className":6203,"style":252},[182,186]," is the price of the house.",[744,6206,6207,6235],{},[50,6208,6210,6223],{"className":6209},[53],[50,6211,6213],{"className":6212},[57],[59,6214,6215],{"xmlns":61},[63,6216,6217,6221],{},[66,6218,6219],{},[80,6220,82],{},[157,6222,82],{"encoding":159},[50,6224,6226],{"className":6225,"ariaHidden":89},[164],[50,6227,6229,6232],{"className":6228},[168],[50,6230],{"className":6231,"style":1528},[172],[50,6233,82],{"className":6234},[182,186]," is the size of the house.",[744,6237,6238,6307],{},[50,6239,6241,6258],{"className":6240},[53],[50,6242,6244],{"className":6243},[57],[59,6245,6246],{"xmlns":61},[63,6247,6248,6256],{},[66,6249,6250],{},[77,6251,6252,6254],{},[80,6253,1074],{},[84,6255,1077],{},[157,6257,1789],{"encoding":159},[50,6259,6261],{"className":6260,"ariaHidden":89},[164],[50,6262,6264,6267],{"className":6263},[168],[50,6265],{"className":6266,"style":691},[172],[50,6268,6270,6273],{"className":6269},[182],[50,6271,1074],{"className":6272,"style":1170},[182,186],[50,6274,6276],{"className":6275},[190],[50,6277,6279,6299],{"className":6278},[194,195],[50,6280,6282,6296],{"className":6281},[199],[50,6283,6285],{"className":6284,"style":204},[203],[50,6286,6287,6290],{"style":1185},[50,6288],{"className":6289,"style":212},[211],[50,6291,6293],{"className":6292},[216,217,218,219],[50,6294,1077],{"className":6295},[182,219],[50,6297,227],{"className":6298},[226],[50,6300,6302],{"className":6301},[199],[50,6303,6305],{"className":6304,"style":234},[203],[50,6306],{}," is the y-intercept.",[744,6309,6310,6379],{},[50,6311,6313,6330],{"className":6312},[53],[50,6314,6316],{"className":6315},[57],[59,6317,6318],{"xmlns":61},[63,6319,6320,6328],{},[66,6321,6322],{},[77,6323,6324,6326],{},[80,6325,1074],{},[84,6327,86],{},[157,6329,4739],{"encoding":159},[50,6331,6333],{"className":6332,"ariaHidden":89},[164],[50,6334,6336,6339],{"className":6335},[168],[50,6337],{"className":6338,"style":691},[172],[50,6340,6342,6345],{"className":6341},[182],[50,6343,1074],{"className":6344,"style":1170},[182,186],[50,6346,6348],{"className":6347},[190],[50,6349,6351,6371],{"className":6350},[194,195],[50,6352,6354,6368],{"className":6353},[199],[50,6355,6357],{"className":6356,"style":204},[203],[50,6358,6359,6362],{"style":1185},[50,6360],{"className":6361,"style":212},[211],[50,6363,6365],{"className":6364},[216,217,218,219],[50,6366,86],{"className":6367},[182,219],[50,6369,227],{"className":6370},[226],[50,6372,6374],{"className":6373},[199],[50,6375,6377],{"className":6376,"style":234},[203],[50,6378],{}," is the slope.",[744,6381,6382,6410],{},[50,6383,6385,6398],{"className":6384},[53],[50,6386,6388],{"className":6387},[57],[59,6389,6390],{"xmlns":61},[63,6391,6392,6396],{},[66,6393,6394],{},[80,6395,1133],{},[157,6397,2114],{"encoding":159},[50,6399,6401],{"className":6400,"ariaHidden":89},[164],[50,6402,6404,6407],{"className":6403},[168],[50,6405],{"className":6406,"style":1528},[172],[50,6408,1133],{"className":6409},[182,186]," is the error or noise, which for this example we will assume is zero for simplicity.",[11,6412,6413,6414,4718,6483,6552],{},"To find the values of ",[50,6415,6417,6434],{"className":6416},[53],[50,6418,6420],{"className":6419},[57],[59,6421,6422],{"xmlns":61},[63,6423,6424,6432],{},[66,6425,6426],{},[77,6427,6428,6430],{},[80,6429,1074],{},[84,6431,1077],{},[157,6433,1789],{"encoding":159},[50,6435,6437],{"className":6436,"ariaHidden":89},[164],[50,6438,6440,6443],{"className":6439},[168],[50,6441],{"className":6442,"style":691},[172],[50,6444,6446,6449],{"className":6445},[182],[50,6447,1074],{"className":6448,"style":1170},[182,186],[50,6450,6452],{"className":6451},[190],[50,6453,6455,6475],{"className":6454},[194,195],[50,6456,6458,6472],{"className":6457},[199],[50,6459,6461],{"className":6460,"style":204},[203],[50,6462,6463,6466],{"style":1185},[50,6464],{"className":6465,"style":212},[211],[50,6467,6469],{"className":6468},[216,217,218,219],[50,6470,1077],{"className":6471},[182,219],[50,6473,227],{"className":6474},[226],[50,6476,6478],{"className":6477},[199],[50,6479,6481],{"className":6480,"style":234},[203],[50,6482],{},[50,6484,6486,6503],{"className":6485},[53],[50,6487,6489],{"className":6488},[57],[59,6490,6491],{"xmlns":61},[63,6492,6493,6501],{},[66,6494,6495],{},[77,6496,6497,6499],{},[80,6498,1074],{},[84,6500,86],{},[157,6502,4739],{"encoding":159},[50,6504,6506],{"className":6505,"ariaHidden":89},[164],[50,6507,6509,6512],{"className":6508},[168],[50,6510],{"className":6511,"style":691},[172],[50,6513,6515,6518],{"className":6514},[182],[50,6516,1074],{"className":6517,"style":1170},[182,186],[50,6519,6521],{"className":6520},[190],[50,6522,6524,6544],{"className":6523},[194,195],[50,6525,6527,6541],{"className":6526},[199],[50,6528,6530],{"className":6529,"style":204},[203],[50,6531,6532,6535],{"style":1185},[50,6533],{"className":6534,"style":212},[211],[50,6536,6538],{"className":6537},[216,217,218,219],[50,6539,86],{"className":6540},[182,219],[50,6542,227],{"className":6543},[226],[50,6545,6547],{"className":6546},[199],[50,6548,6550],{"className":6549,"style":234},[203],[50,6551],{},", we can use the least squares method with the formula we mentioned earlier:",[50,6554,6556],{"className":6555},[650],[50,6557,6559,6656],{"className":6558},[53],[50,6560,6562],{"className":6561},[57],[59,6563,6564],{"xmlns":61,"display":659},[63,6565,6566,6654],{},[66,6567,6568,6574,6576],{},[77,6569,6570,6572],{},[80,6571,1074],{},[84,6573,86],{},[69,6575,1069],{},[3192,6577,6578,6618],{},[66,6579,6580,6582,6584,6586,6592,6598,6600,6602,6604,6610,6612],{},[80,6581,142],{},[69,6583,3203],{},[69,6585,75],{"stretchy":71},[77,6587,6588,6590],{},[80,6589,82],{},[80,6591,519],{},[77,6593,6594,6596],{},[80,6595,95],{},[80,6597,519],{},[69,6599,100],{"stretchy":71},[69,6601,2587],{},[69,6603,3203],{},[77,6605,6606,6608],{},[80,6607,82],{},[80,6609,519],{},[69,6611,3203],{},[77,6613,6614,6616],{},[80,6615,95],{},[80,6617,519],{},[66,6619,6620,6622,6624,6626,6634,6636,6638,6640,6642,6648],{},[80,6621,142],{},[69,6623,3203],{},[69,6625,75],{"stretchy":71},[4864,6627,6628,6630,6632],{},[80,6629,82],{},[80,6631,519],{},[84,6633,111],{},[69,6635,100],{"stretchy":71},[69,6637,2587],{},[69,6639,75],{"stretchy":71},[69,6641,3203],{},[77,6643,6644,6646],{},[80,6645,82],{},[80,6647,519],{},[3235,6649,6650,6652],{},[69,6651,100],{"stretchy":71},[84,6653,111],{},[157,6655,4894],{"encoding":159},[50,6657,6659,6714],{"className":6658,"ariaHidden":89},[164],[50,6660,6662,6665,6705,6708,6711],{"className":6661},[168],[50,6663],{"className":6664,"style":691},[172],[50,6666,6668,6671],{"className":6667},[182],[50,6669,1074],{"className":6670,"style":1170},[182,186],[50,6672,6674],{"className":6673},[190],[50,6675,6677,6697],{"className":6676},[194,195],[50,6678,6680,6694],{"className":6679},[199],[50,6681,6683],{"className":6682,"style":204},[203],[50,6684,6685,6688],{"style":1185},[50,6686],{"className":6687,"style":212},[211],[50,6689,6691],{"className":6690},[216,217,218,219],[50,6692,86],{"className":6693},[182,219],[50,6695,227],{"className":6696},[226],[50,6698,6700],{"className":6699},[199],[50,6701,6703],{"className":6702,"style":234},[203],[50,6704],{},[50,6706],{"className":6707,"style":699},[244],[50,6709,1069],{"className":6710},[703],[50,6712],{"className":6713,"style":699},[244],[50,6715,6717,6720],{"className":6716},[168],[50,6718],{"className":6719,"style":4959},[172],[50,6721,6723,6726,7125],{"className":6722},[182],[50,6724],{"className":6725},[177,3286],[50,6727,6729],{"className":6728},[3192],[50,6730,6732,7117],{"className":6731},[194,195],[50,6733,6735,7114],{"className":6734},[199],[50,6736,6738,6899,6907],{"className":6737,"style":4978},[203],[50,6739,6740,6743],{"style":3302},[50,6741],{"className":6742,"style":2754},[211],[50,6744,6746,6749,6752,6755,6758,6809,6812,6815,6818,6821,6824,6827,6830,6870],{"className":6745},[182],[50,6747,142],{"className":6748},[182,186],[50,6750],{"className":6751,"style":245},[244],[50,6753,3203],{"className":6754,"style":4997},[3356,3401,4996],[50,6756,75],{"className":6757},[177],[50,6759,6761,6764],{"className":6760},[182],[50,6762,82],{"className":6763},[182,186],[50,6765,6767],{"className":6766},[190],[50,6768,6770,6801],{"className":6769},[194,195],[50,6771,6773,6798],{"className":6772},[199],[50,6774,6776,6787],{"className":6775,"style":5019},[203],[50,6777,6778,6781],{"style":5022},[50,6779],{"className":6780,"style":212},[211],[50,6782,6784],{"className":6783},[216,217,218,219],[50,6785,519],{"className":6786},[182,186,219],[50,6788,6789,6792],{"style":5034},[50,6790],{"className":6791,"style":212},[211],[50,6793,6795],{"className":6794},[216,217,218,219],[50,6796,111],{"className":6797},[182,219],[50,6799,227],{"className":6800},[226],[50,6802,6804],{"className":6803},[199],[50,6805,6807],{"className":6806,"style":5053},[203],[50,6808],{},[50,6810,100],{"className":6811},[291],[50,6813],{"className":6814,"style":736},[244],[50,6816,2587],{"className":6817},[1212],[50,6819],{"className":6820,"style":736},[244],[50,6822,75],{"className":6823},[177],[50,6825,3203],{"className":6826,"style":4997},[3356,3401,4996],[50,6828],{"className":6829,"style":245},[244],[50,6831,6833,6836],{"className":6832},[182],[50,6834,82],{"className":6835},[182,186],[50,6837,6839],{"className":6838},[190],[50,6840,6842,6862],{"className":6841},[194,195],[50,6843,6845,6859],{"className":6844},[199],[50,6846,6848],{"className":6847,"style":551},[203],[50,6849,6850,6853],{"style":207},[50,6851],{"className":6852,"style":212},[211],[50,6854,6856],{"className":6855},[216,217,218,219],[50,6857,519],{"className":6858},[182,186,219],[50,6860,227],{"className":6861},[226],[50,6863,6865],{"className":6864},[199],[50,6866,6868],{"className":6867,"style":234},[203],[50,6869],{},[50,6871,6873,6876],{"className":6872},[291],[50,6874,100],{"className":6875},[291],[50,6877,6879],{"className":6878},[190],[50,6880,6882],{"className":6881},[194],[50,6883,6885],{"className":6884},[199],[50,6886,6888],{"className":6887,"style":5135},[203],[50,6889,6890,6893],{"style":5138},[50,6891],{"className":6892,"style":212},[211],[50,6894,6896],{"className":6895},[216,217,218,219],[50,689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is the number of examples (in this case, 3).",[744,7401,7402,4718,7471,7540],{},[50,7403,7405,7422],{"className":7404},[53],[50,7406,7408],{"className":7407},[57],[59,7409,7410],{"xmlns":61},[63,7411,7412,7420],{},[66,7413,7414],{},[77,7415,7416,7418],{},[80,7417,82],{},[80,7419,519],{},[157,7421,522],{"encoding":159},[50,7423,7425],{"className":7424,"ariaHidden":89},[164],[50,7426,7428,7431],{"className":7427},[168],[50,7429],{"className":7430,"style":532},[172],[50,7432,7434,7437],{"className":7433},[182],[50,7435,82],{"className":7436},[182,186],[50,7438,7440],{"className":7439},[190],[50,7441,7443,7463],{"className":7442},[194,195],[50,7444,7446,7460],{"className":7445},[199],[50,7447,7449],{"className":7448,"style":551},[203],[50,7450,7451,7454],{"style":207},[50,7452],{"className":7453,"style":212},[211],[50,7455,7457],{"className":7456},[216,217,218,219],[50,7458,519],{"className":7459},[182,186,219],[50,7461,227],{"className":7462},[226],[50,7464,7466],{"className":7465},[199],[50,7467,7469],{"className":7468,"style":234},[203],[50,7470],{},[50,7472,7474,7491],{"className":7473},[53],[50,7475,7477],{"className":7476},[57],[59,7478,7479],{"xmlns":61},[63,7480,7481,7489],{},[66,7482,7483],{},[77,7484,7485,7487],{},[80,7486,95],{},[80,7488,519],{},[157,7490,595],{"encoding":159},[50,7492,7494],{"className":7493,"ariaHidden":89},[164],[50,7495,7497,7500],{"className":7496},[168],[50,7498],{"className":7499,"style":605},[172],[50,7501,7503,7506],{"className":7502},[182],[50,7504,95],{"className":7505,"style":252},[182,186],[50,7507,7509],{"className":7508},[190],[50,7510,7512,7532],{"className":7511},[194,195],[50,7513,7515,7529],{"className":7514},[199],[50,7516,7518],{"className":7517,"style":551},[203],[50,7519,7520,7523],{"style":267},[50,7521],{"className":7522,"style":212},[211],[50,7524,7526],{"className":7525},[216,217,218,219],[50,7527,519],{"className":7528},[182,186,219],[50,7530,227],{"className":7531},[226],[50,7533,7535],{"className":7534},[199],[50,7536,7538],{"className":7537,"style":234},[203],[50,7539],{}," are the values of size and price for each example.",[744,7542,7543,4718,7603,7674],{},[50,7544,7546,7563],{"className":7545},[53],[50,7547,7549],{"className":7548},[57],[59,7550,7551],{"xmlns":61},[63,7552,7553,7561],{},[66,7554,7555],{},[2589,7556,7557,7559],{"accent":89},[80,7558,82],{},[69,7560,5407],{},[157,7562,5816],{"encoding":159},[50,7564,7566],{"className":7565,"ariaHidden":89},[164],[50,7567,7569,7572],{"className":7568},[168],[50,7570],{"className":7571,"style":5501},[172],[50,7573,7575],{"className":7574},[182,2737],[50,7576,7578],{"className":7577},[194],[50,7579,7581],{"className":7580},[199],[50,7582,7584,7592],{"className":7583,"style":5501},[203],[50,7585,7586,7589],{"style":2750},[50,7587],{"className":7588,"style":2754},[211],[50,7590,82],{"className":7591},[182,186],[50,7593,7594,7597],{"style":2750},[50,7595],{"className":7596,"style":2754},[211],[50,7598,7600],{"className":7599,"style":5615},[2804],[50,7601,5407],{"className":7602},[182],[50,7604,7606,7623],{"className":7605},[53],[50,7607,7609],{"className":7608},[57],[59,7610,7611],{"xmlns":61},[63,7612,7613,7621],{},[66,7614,7615],{},[2589,7616,7617,7619],{"accent":89},[80,7618,95],{},[69,7620,5407],{},[157,7622,5877],{"encoding":159},[50,7624,7626],{"className":7625,"ariaHidden":89},[164],[50,7627,7629,7632],{"className":7628},[168],[50,7630],{"className":7631,"style":5887},[172],[50,7633,7635],{"className":7634},[182,2737],[50,7636,7638,7666],{"className":7637},[194,195],[50,7639,7641,7663],{"className":7640},[199],[50,7642,7644,7652],{"className":7643,"style":5501},[203],[50,7645,7646,7649],{"style":2750},[50,7647],{"className":7648,"style":2754},[211],[50,7650,95],{"className":7651,"style":252},[182,186],[50,7653,7654,7657],{"style":2750},[50,7655],{"className":7656,"style":2754},[211],[50,7658,7660],{"className":7659,"style":3803},[2804],[50,7661,5407],{"className":7662},[182],[50,7664,227],{"className":7665},[226],[50,7667,7669],{"className":7668},[199],[50,7670,7672],{"className":7671,"style":2818},[203],[50,7673],{}," are the means of the independent and dependent variables, respectively.",[11,7676,7677],{},"We have then for this case:",[50,7679,7681],{"className":7680},[650],[50,7682,7684,7821],{"className":7683},[53],[50,7685,7687],{"className":7686},[57],[59,7688,7689],{"xmlns":61,"display":659},[63,7690,7691,7818],{},[66,7692,7693,7699,7701],{},[77,7694,7695,7697],{},[80,7696,1074],{},[84,7698,86],{},[69,7700,1069],{},[3192,7702,7703,7768],{},[66,7704,7705,7708,7710,7712,7715,7718,7720,7722,7724,7727,7729,7731,7733,7736,7738,7740,7742,7744,7746,7748,7750,7752,7754,7756,7758,7760,7762,7764,7766],{},[84,7706,7707],{},"3",[69,7709,75],{"stretchy":71},[84,7711,5958],{},[69,7713,7714],{},"∗",[84,7716,7717],{},"100000",[69,7719,1080],{},[84,7721,5966],{},[69,7723,7714],{},[84,7725,7726],{},"200000",[69,7728,1080],{},[84,7730,5974],{},[69,7732,7714],{},[84,7734,7735],{},"300000",[69,7737,100],{"stretchy":71},[69,7739,2587],{},[69,7741,75],{"stretchy":71},[84,7743,5958],{},[69,7745,1080],{},[84,7747,5966],{},[69,7749,1080],{},[84,7751,5974],{},[69,7753,100],{"stretchy":71},[69,7755,75],{"stretchy":71},[84,7757,7717],{},[69,7759,1080],{},[84,7761,7726],{},[69,7763,1080],{},[84,7765,7735],{},[69,7767,100],{"stretchy":71},[66,7769,7770,7772,7774,7780,7782,7788,7790,7796,7798,7800,7802,7804,7806,7808,7810,7812],{},[84,7771,7707],{},[69,7773,75],{"stretchy":71},[3235,7775,7776,7778],{},[84,7777,5958],{},[84,7779,111],{},[69,7781,1080],{},[3235,7783,7784,7786],{},[84,7785,5966],{},[84,7787,111],{},[69,7789,1080],{},[3235,7791,7792,7794],{},[84,7793,5974],{},[84,7795,111],{},[69,7797,100],{"stretchy":71},[69,7799,2587],{},[69,7801,75],{"stretchy":71},[84,7803,5958],{},[69,7805,1080],{},[84,7807,5966],{},[69,7809,1080],{},[84,7811,5974],{},[3235,7813,7814,7816],{},[69,7815,100],{"stretchy":71},[84,7817,111],{},[157,7819,7820],{"encoding":159},"\\beta_1 = \\frac{3(50*100000 + 100*200000 + 150*300000) - (50 + 100 + 150)(100000 + 200000 + 300000)}{3(50^2 + 100^2 + 150^2) - (50 + 100 + 150)^2}",[50,7822,7824,7879],{"className":7823,"ariaHidden":89},[164],[50,7825,7827,7830,7870,7873,7876],{"className":7826},[168],[50,7828],{"className":7829,"style":691},[172],[50,7831,7833,7836],{"className":7832},[182],[50,7834,1074],{"className":7835,"style":1170},[182,186],[50,7837,7839],{"className":7838},[190],[50,7840,7842,7862],{"className":7841},[194,195],[50,7843,7845,7859],{"className":7844},[199],[50,7846,7848],{"className":7847,"style":204},[203],[50,7849,7850,7853],{"style":1185},[50,7851],{"className":7852,"style":212},[211],[50,7854,7856],{"className":7855},[216,217,218,219],[50,7857,86],{"className":7858},[182,219],[50,7860,227],{"className":7861},[226],[50,7863,7865],{"className":7864},[199],[50,7866,7868],{"className":7867,"style":234},[203],[50,7869],{},[50,7871],{"className":7872,"style":699},[244],[50,7874,1069],{"className":7875},[703],[50,7877],{"className":7878,"style":699},[244],[50,7880,7882,7886],{"className":7881},[168],[50,7883],{"className":7884,"style":7885},[172],"height:2.363em;vertical-align:-0.936em;",[50,7887,7889,7892,8281],{"className":7888},[182],[50,7890],{"className":7891},[177,3286],[50,7893,7895],{"className":7894},[3192],[50,7896,7898,8272],{"className":7897},[194,195],[50,7899,7901,8269],{"className":7900},[199],[50,7902,7904,8106,8114],{"className":7903,"style":4978},[203],[50,7905,7906,7909],{"style":3302},[50,7907],{"className":7908,"style":2754},[211],[50,7910,7912,7915,7918,7922,7951,7954,7957,7960,7964,7993,7996,7999,8002,8006,8035,8038,8041,8044,8047,8050,8053,8056,8059,8062,8065,8068,8071,8074,8077],{"className":7911},[182],[50,7913,7707],{"className":7914},[182],[50,7916,75],{"className":7917},[177],[50,7919,7921],{"className":7920},[182],"5",[50,7923,7925,7928],{"className":7924},[182],[50,7926,1077],{"className":7927},[182],[50,7929,7931],{"className":7930},[190],[50,7932,7934],{"className":7933},[194],[50,7935,7937],{"className":7936},[199],[50,7938,7940],{"className":7939,"style":5135},[203],[50,7941,7942,7945],{"style":5138},[50,7943],{"className":7944,"style":212},[211],[50,7946,7948],{"className":7947},[216,217,218,219],[50,7949,111],{"className":7950},[182,219],[50,7952],{"className":7953,"style":736},[244],[50,7955,1080],{"className":7956},[1212],[50,7958],{"className":7959,"style":736},[244],[50,7961,7963],{"className":7962},[182],"10",[50,7965,7967,7970],{"className":7966},[182],[50,7968,1077],{"className":7969},[182],[50,7971,7973],{"className":7972},[190],[50,7974,7976],{"className":7975},[194],[50,7977,7979],{"className":7978},[199],[50,7980,7982],{"className":7981,"style":5135},[203],[50,7983,7984,7987],{"style":5138},[50,7985],{"className":7986,"style":212},[211],[50,7988,7990],{"className":7989},[216,217,218,219],[50,7991,111],{"className":7992},[182,219],[50,7994],{"className":7995,"style":736},[244],[50,7997,1080],{"className":7998},[1212],[50,8000],{"className":8001,"style":736},[244],[50,8003,8005],{"className":8004},[182],"15",[50,8007,8009,8012],{"className":8008},[182],[50,8010,1077],{"className":8011},[182],[50,8013,8015],{"className":8014},[190],[50,8016,8018],{"className":8017},[194],[50,8019,8021],{"className":8020},[199],[50,8022,8024],{"className":8023,"style":5135},[203],[50,8025,8026,8029],{"style":5138},[50,8027],{"className":8028,"style":212},[211],[50,8030,8032],{"className":8031},[216,217,218,219],[50,8033,111],{"className":8034},[182,219],[50,8036,100],{"className":8037},[291],[50,8039],{"className":8040,"style":736},[244],[50,8042,2587],{"className":8043},[1212],[50,8045],{"className":8046,"style":736},[244],[50,8048,75],{"className":8049},[177],[50,8051,5958],{"className":8052},[182],[50,8054],{"className":8055,"style":736},[244],[50,8057,1080],{"className":8058},[1212],[50,8060],{"className":8061,"style":736},[244],[50,8063,5966],{"className":8064},[182],[50,8066],{"className":8067,"style":736},[244],[50,8069,1080],{"className":8070},[1212],[50,8072],{"className":8073,"style":736},[244],[50,8075,5974],{"className":8076},[182],[50,8078,8080,8083],{"className":8079},[291],[50,8081,100],{"className":8082},[291],[50,8084,8086],{"className":8085},[190],[50,8087,8089],{"className":8088},[194],[50,8090,8092],{"className":8091},[199],[50,8093,8095],{"className":8094,"style":5135},[203],[50,8096,8097,8100],{"style":5138},[50,8098],{"className":8099,"style":212},[211],[50,8101,8103],{"className":8102},[216,217,218,219],[50,8104,111],{"className":8105},[182,219],[50,8107,8108,8111],{"style":3314},[50,8109],{"className":8110,"style":2754},[211],[50,8112],{"className":8113,"style":3322},[3321],[50,8115,8116,8119],{"style":3325},[50,8117],{"className":8118,"style":2754},[211],[50,8120,8122,8125,8128,8131,8134,8137,8140,8143,8146,8149,8152,8155,8158,8161,8164,8167,8170,8173,8176,8179,8182,8185,8188,8191,8194,8197,8200,8203,8206,8209,8212,8215,8218,8221,8224,8227,8230,8233,8236,8239,8242,8245,8248,8251,8254,8257,8260,8263,8266],{"className":8121},[182],[50,8123,7707],{"className":8124},[182],[50,8126,75],{"className":8127},[177],[50,8129,5958],{"className":8130},[182],[50,8132],{"className":8133,"style":736},[244],[50,8135,7714],{"className":8136},[1212],[50,8138],{"className":8139,"style":736},[244],[50,8141,7717],{"className":8142},[182],[50,8144],{"className":8145,"style":736},[244],[50,8147,1080],{"className":8148},[1212],[50,8150],{"className":8151,"style":736},[244],[50,8153,5966],{"className":8154},[182],[50,8156],{"className":8157,"style":736},[244],[50,8159,7714],{"className":8160},[1212],[50,8162],{"className":8163,"style":736},[244],[50,8165,7726],{"className":8166},[182],[50,8168],{"className":8169,"style":736},[244],[50,8171,1080],{"className":8172},[1212],[50,8174],{"className":8175,"style":736},[244],[50,8177,5974],{"className":8178},[182],[50,8180],{"className":8181,"style":736},[244],[50,8183,7714],{"className":8184},[1212],[50,8186],{"className":8187,"style":736},[244],[50,8189,7735],{"className":8190},[182],[50,8192,100],{"className":8193},[291],[50,8195],{"className":8196,"style":736},[244],[50,8198,2587],{"className":8199},[1212],[50,8201],{"className":8202,"style":736},[244],[50,8204,75],{"className":8205},[177],[50,8207,5958],{"className":8208},[182],[50,8210],{"className":8211,"style":736},[244],[50,8213,1080],{"className":8214},[1212],[50,8216],{"className":8217,"style":736},[244],[50,8219,5966],{"className":8220},[182],[50,8222],{"className":8223,"style":736},[244],[50,8225,1080],{"className":8226},[1212],[50,8228],{"className":8229,"style":736},[244],[50,8231,5974],{"className":8232},[182],[50,8234,100],{"className":8235},[291],[50,8237,75],{"className":8238},[177],[50,8240,7717],{"className":8241},[182],[50,8243],{"className":8244,"style":736},[244],[50,8246,1080],{"className":8247},[1212],[50,8249],{"className":8250,"style":736},[244],[50,8252,7726],{"className":8253},[182],[50,8255],{"className":8256,"style":736},[244],[50,8258,1080],{"className":8259},[1212],[50,8261],{"className":8262,"style":736},[244],[50,8264,7735],{"className":8265},[182],[50,8267,100],{"className":8268},[291],[50,8270,227],{"className":8271},[226],[50,8273,8275],{"className":8274},[199],[50,8276,8279],{"className":8277,"style":8278},[203],"height:0.936em;",[50,8280],{},[50,8282],{"className":8283},[291,3286],[50,8285,8287],{"className":8286},[650],[50,8288,8290,8350],{"className":8289},[53],[50,8291,8293],{"className":8292},[57],[59,8294,8295],{"xmlns":61,"display":659},[63,8296,8297,8347],{},[66,8298,8299,8305,8307,8323,8325,8331],{},[77,8300,8301,8303],{},[80,8302,1074],{},[84,8304,1077],{},[69,8306,1069],{},[3192,8308,8309,8321],{},[66,8310,8311,8313,8315,8317,8319],{},[84,8312,7717],{},[69,8314,1080],{},[84,8316,7726],{},[69,8318,1080],{},[84,8320,7735],{},[84,8322,7707],{},[69,8324,2587],{},[77,8326,8327,8329],{},[80,8328,1074],{},[84,8330,86],{},[3192,8332,8333,8345],{},[66,8334,8335,8337,8339,8341,8343],{},[84,8336,5958],{},[69,8338,1080],{},[84,8340,5966],{},[69,8342,1080],{},[84,8344,5974],{},[84,8346,7707],{},[157,8348,8349],{"encoding":159},"\\beta_0 = \\frac{100000 + 200000 + 300000}{3} - \\beta_1 \\frac{50 + 100 + 150}{3}",[50,8351,8353,8408,8510],{"className":8352,"ariaHidden":89},[164],[50,8354,8356,8359,8399,8402,8405],{"className":8355},[168],[50,8357],{"className":8358,"style":691},[172],[50,8360,8362,8365],{"className":8361},[182],[50,8363,1074],{"className":8364,"style":1170},[182,186],[50,8366,8368],{"className":8367},[190],[50,8369,8371,8391],{"className":8370},[194,195],[50,8372,8374,8388],{"className":8373},[199],[50,8375,8377],{"className":8376,"style":204},[203],[50,8378,8379,8382],{"style":1185},[50,8380],{"className":8381,"style":212},[211],[50,8383,8385],{"className":8384},[216,217,218,219],[50,8386,1077],{"className":8387},[182,219],[50,8389,227],{"className":8390},[226],[50,8392,8394],{"className":8393},[199],[50,8395,8397],{"className":8396,"style":234},[203],[50,8398],{},[50,8400],{"className":8401,"style":699},[244],[50,8403,1069],{"className":8404},[703],[50,8406],{"className":8407,"style":699},[244],[50,8409,8411,8415,8501,8504,8507],{"className":8410},[168],[50,8412],{"className":8413,"style":8414},[172],"height:2.0074em;vertical-align:-0.686em;",[50,8416,8418,8421,8498],{"className":8417},[182],[50,8419],{"className":8420},[177,3286],[50,8422,8424],{"className":8423},[3192],[50,8425,8427,8490],{"className":8426},[194,195],[50,8428,8430,8487],{"className":8429},[199],[50,8431,8433,8444,8452],{"className":8432,"style":3299},[203],[50,8434,8435,8438],{"style":3302},[50,8436],{"className":8437,"style":2754},[211],[50,8439,8441],{"className":8440},[182],[50,8442,7707],{"className":8443},[182],[50,8445,8446,8449],{"style":3314},[50,8447],{"className":8448,"style":2754},[211],[50,8450],{"className":8451,"style":3322},[3321],[50,8453,8454,8457],{"style":3325},[50,8455],{"className":8456,"style":2754},[211],[50,8458,8460,8463,8466,8469,8472,8475,8478,8481,8484],{"className":8459},[182],[50,8461,7717],{"className":8462},[182],[50,8464],{"className":8465,"style":736},[244],[50,8467,1080],{"className":8468},[1212],[50,8470],{"className":8471,"style":736},[244],[50,8473,7726],{"className":8474},[182],[50,8476],{"className":8477,"style":736},[244],[50,8479,1080],{"className":8480},[1212],[50,8482],{"className":8483,"style":736},[244],[50,8485,7735],{"className":8486},[182],[50,8488,227],{"className":8489},[226],[50,8491,8493],{"className":8492},[199],[50,8494,8496],{"className":8495,"style":3344},[203],[50,8497],{},[50,8499],{"className":8500},[291,3286],[50,8502],{"className":8503,"style":736},[244],[50,8505,2587],{"className":8506},[1212],[50,8508],{"className":8509,"style":736},[244],[50,8511,8513,8516,8556],{"className":8512},[168],[50,8514],{"className":8515,"style":8414},[172],[50,8517,8519,8522],{"className":8518},[182],[50,8520,1074],{"className":8521,"style":1170},[182,186],[50,8523,8525],{"className":8524},[190],[50,8526,8528,8548],{"className":8527},[194,195],[50,8529,8531,8545],{"className":8530},[199],[50,8532,8534],{"className":8533,"style":204},[203],[50,8535,8536,8539],{"style":1185},[50,8537],{"className":8538,"style":212},[211],[50,8540,8542],{"className":8541},[216,217,218,219],[50,8543,86],{"className":8544},[182,219],[50,8546,227],{"className":8547},[226],[50,8549,8551],{"className":8550},[199],[50,8552,8554],{"className":8553,"style":234},[203],[50,8555],{},[50,8557,8559,8562,8639],{"className":8558},[182],[50,8560],{"className":8561},[177,3286],[50,8563,8565],{"className":8564},[3192],[50,8566,8568,8631],{"className":8567},[194,195],[50,8569,8571,8628],{"className":8570},[199],[50,8572,8574,8585,8593],{"className":8573,"style":3299},[203],[50,8575,8576,8579],{"style":3302},[50,8577],{"className":8578,"style":2754},[211],[50,8580,8582],{"className":8581},[182],[50,8583,7707],{"className":8584},[182],[50,8586,8587,8590],{"style":3314},[50,8588],{"className":8589,"style":2754},[211],[50,8591],{"className":8592,"style":3322},[3321],[50,8594,8595,8598],{"style":3325},[50,8596],{"className":8597,"style":2754},[211],[50,8599,8601,8604,8607,8610,8613,8616,8619,8622,8625],{"className":8600},[182],[50,8602,5958],{"className":8603},[182],[50,8605],{"className":8606,"style":736},[244],[50,8608,1080],{"className":8609},[1212],[50,8611],{"className":8612,"style":736},[244],[50,8614,5966],{"className":8615},[182],[50,8617],{"className":8618,"style":736},[244],[50,8620,1080],{"className":8621},[1212],[50,8623],{"className":8624,"style":736},[244],[50,8626,5974],{"className":8627},[182],[50,8629,227],{"className":8630},[226],[50,8632,8634],{"className":8633},[199],[50,8635,8637],{"className":8636,"style":3344},[203],[50,8638],{},[50,8640],{"className":8641},[291,3286],[11,8643,8644,8645,4718,8738,8831],{},"Solving these formulas, we obtain the values ​​of ",[50,8646,8648,8670],{"className":8647},[53],[50,8649,8651],{"className":8650},[57],[59,8652,8653],{"xmlns":61},[63,8654,8655,8667],{},[66,8656,8657,8663,8665],{},[77,8658,8659,8661],{},[80,8660,1074],{},[84,8662,1077],{},[69,8664,1069],{},[84,8666,1077],{},[157,8668,8669],{"encoding":159},"\\beta_0 = 0",[50,8671,8673,8728],{"className":8672,"ariaHidden":89},[164],[50,8674,8676,8679,8719,8722,8725],{"className":8675},[168],[50,8677],{"className":8678,"style":691},[172],[50,8680,8682,8685],{"className":8681},[182],[50,8683,1074],{"className":8684,"style":1170},[182,186],[50,8686,8688],{"className":8687},[190],[50,8689,8691,8711],{"className":8690},[194,195],[50,8692,8694,8708],{"className":8693},[199],[50,8695,8697],{"className":8696,"style":204},[203],[50,8698,8699,8702],{"style":1185},[50,8700],{"className":8701,"style":212},[211],[50,8703,8705],{"className":8704},[216,217,218,219],[50,8706,1077],{"className":8707},[182,219],[50,8709,227],{"className":8710},[226],[50,8712,8714],{"className":8713},[199],[50,8715,8717],{"className":8716,"style":234},[203],[50,8718],{},[50,8720],{"className":8721,"style":699},[244],[50,8723,1069],{"className":8724},[703],[50,8726],{"className":8727,"style":699},[244],[50,8729,8731,8735],{"className":8730},[168],[50,8732],{"className":8733,"style":8734},[172],"height:0.6444em;",[50,8736,1077],{"className":8737},[182],[50,8739,8741,8764],{"className":8740},[53],[50,8742,8744],{"className":8743},[57],[59,8745,8746],{"xmlns":61},[63,8747,8748,8761],{},[66,8749,8750,8756,8758],{},[77,8751,8752,8754],{},[80,8753,1074],{},[84,8755,86],{},[69,8757,1069],{},[84,8759,8760],{},"2000",[157,8762,8763],{"encoding":159},"\\beta_1 = 2000",[50,8765,8767,8822],{"className":8766,"ariaHidden":89},[164],[50,8768,8770,8773,8813,8816,8819],{"className":8769},[168],[50,8771],{"className":8772,"style":691},[172],[50,8774,8776,8779],{"className":8775},[182],[50,8777,1074],{"className":8778,"style":1170},[182,186],[50,8780,8782],{"className":8781},[190],[50,8783,8785,8805],{"className":8784},[194,195],[50,8786,8788,8802],{"className":8787},[199],[50,8789,8791],{"className":8790,"style":204},[203],[50,8792,8793,8796],{"style":1185},[50,8794],{"className":8795,"style":212},[211],[50,8797,8799],{"className":8798},[216,217,218,219],[50,8800,86],{"className":8801},[182,219],[50,8803,227],{"className":8804},[226],[50,8806,8808],{"className":8807},[199],[50,8809,8811],{"className":8810,"style":234},[203],[50,8812],{},[50,8814],{"className":8815,"style":699},[244],[50,8817,1069],{"className":8818},[703],[50,8820],{"className":8821,"style":699},[244],[50,8823,8825,8828],{"className":8824},[168],[50,8826],{"className":8827,"style":8734},[172],[50,8829,8760],{"className":8830},[182],", which allow us to build the following model:",[50,8833,8835],{"className":8834},[650],[50,8836,8838,8866],{"className":8837},[53],[50,8839,8841],{"className":8840},[57],[59,8842,8843],{"xmlns":61,"display":659},[63,8844,8845,8863],{},[66,8846,8847,8853,8855,8857,8859,8861],{},[2589,8848,8849,8851],{"accent":89},[80,8850,95],{},[69,8852,2599],{},[69,8854,1069],{},[84,8856,1077],{},[69,8858,1080],{},[84,8860,8760],{},[80,8862,82],{},[157,8864,8865],{"encoding":159},"\\hat{y} = 0 + 2000x",[50,8867,8869,8926,8945],{"className":8868,"ariaHidden":89},[164],[50,8870,8872,8875,8917,8920,8923],{"className":8871},[168],[50,8873],{"className":8874,"style":691},[172],[50,8876,8878],{"className":8877},[182,2737],[50,8879,8881,8909],{"className":8880},[194,195],[50,8882,8884,8906],{"className":8883},[199],[50,8885,8887,8895],{"className":8886,"style":3501},[203],[50,8888,8889,8892],{"style":2750},[50,8890],{"className":8891,"style":2754},[211],[50,8893,95],{"className":8894,"style":252},[182,186],[50,8896,8897,8900],{"style":2750},[50,8898],{"className":8899,"style":2754},[211],[50,8901,8903],{"className":8902,"style":3803},[2804],[50,8904,2599],{"className":8905},[182],[50,8907,227],{"className":8908},[226],[50,8910,8912],{"className":8911},[199],[50,8913,8915],{"className":8914,"style":2818},[203],[50,8916],{},[50,8918],{"className":8919,"style":699},[244],[50,8921,1069],{"className":8922},[703],[50,8924],{"className":8925,"style":699},[244],[50,8927,8929,8933,8936,8939,8942],{"className":8928},[168],[50,8930],{"className":8931,"style":8932},[172],"height:0.7278em;vertical-align:-0.0833em;",[50,8934,1077],{"className":8935},[182],[50,8937],{"className":8938,"style":736},[244],[50,8940,1080],{"className":8941},[1212],[50,8943],{"className":8944,"style":736},[244],[50,8946,8948,8951,8954],{"className":8947},[168],[50,8949],{"className":8950,"style":8734},[172],[50,8952,8760],{"className":8953},[182],[50,8955,82],{"className":8956},[182,186],[50,8958,8960],{"className":8959},[650],[50,8961,8963,8987],{"className":8962},[53],[50,8964,8966],{"className":8965},[57],[59,8967,8968],{"xmlns":61,"display":659},[63,8969,8970,8984],{},[66,8971,8972,8978,8980,8982],{},[2589,8973,8974,8976],{"accent":89},[80,8975,95],{},[69,8977,2599],{},[69,8979,1069],{},[84,8981,8760],{},[80,8983,82],{},[157,8985,8986],{"encoding":159},"\\hat{y} = 2000x",[50,8988,8990,9047],{"className":8989,"ariaHidden":89},[164],[50,8991,8993,8996,9038,9041,9044],{"className":8992},[168],[50,8994],{"className":8995,"style":691},[172],[50,8997,8999],{"className":8998},[182,2737],[50,9000,9002,9030],{"className":9001},[194,195],[50,9003,9005,9027],{"className":9004},[199],[50,9006,9008,9016],{"className":9007,"style":3501},[203],[50,9009,9010,9013],{"style":2750},[50,9011],{"className":9012,"style":2754},[211],[50,9014,95],{"className":9015,"style":252},[182,186],[50,9017,9018,9021],{"style":2750},[50,9019],{"className":9020,"style":2754},[211],[50,9022,9024],{"className":9023,"style":3803},[2804],[50,9025,2599],{"className":9026},[182],[50,9028,227],{"className":9029},[226],[50,9031,9033],{"className":9032},[199],[50,9034,9036],{"className":9035,"style":2818},[203],[50,9037],{},[50,9039],{"className":9040,"style":699},[244],[50,9042,1069],{"className":9043},[703],[50,9045],{"className":9046,"style":699},[244],[50,9048,9050,9053,9056],{"className":9049},[168],[50,9051],{"className":9052,"style":8734},[172],[50,9054,8760],{"className":9055},[182],[50,9057,82],{"className":9058},[182,186],[11,9060,9061],{},"We can now make predictions for new size values. For example, for a 120 m² house, the model predicts a price of:",[50,9063,9065],{"className":9064},[650],[50,9066,9068,9105],{"className":9067},[53],[50,9069,9071],{"className":9070},[57],[59,9072,9073],{"xmlns":61,"display":659},[63,9074,9075,9102],{},[66,9076,9077,9083,9085,9087,9089,9092,9094,9097,9099],{},[2589,9078,9079,9081],{"accent":89},[80,9080,95],{},[69,9082,2599],{},[69,9084,1069],{},[84,9086,8760],{},[69,9088,7714],{},[84,9090,9091],{},"120",[69,9093,1069],{},[84,9095,9096],{},"240",[69,9098,90],{"separator":89},[84,9100,9101],{},"000",[157,9103,9104],{"encoding":159},"\\hat{y} = 2000 * 120 = 240,000",[50,9106,9108,9165,9183,9201],{"className":9107,"ariaHidden":89},[164],[50,9109,9111,9114,9156,9159,9162],{"className":9110},[168],[50,9112],{"className":9113,"style":691},[172],[50,9115,9117],{"className":9116},[182,2737],[50,9118,9120,9148],{"className":9119},[194,195],[50,9121,9123,9145],{"className":9122},[199],[50,9124,9126,9134],{"className":9125,"style":3501},[203],[50,9127,9128,9131],{"style":2750},[50,9129],{"className":9130,"style":2754},[211],[50,9132,95],{"className":9133,"style":252},[182,186],[50,9135,9136,9139],{"style":2750},[50,9137],{"className":9138,"style":2754},[211],[50,9140,9142],{"className":9141,"style":3803},[2804],[50,9143,2599],{"className":9144},[182],[50,9146,227],{"className":9147},[226],[50,9149,9151],{"className":9150},[199],[50,9152,9154],{"className":9153,"style":2818},[203],[50,9155],{},[50,9157],{"className":9158,"style":699},[244],[50,9160,1069],{"className":9161},[703],[50,9163],{"className":9164,"style":699},[244],[50,9166,9168,9171,9174,9177,9180],{"className":9167},[168],[50,9169],{"className":9170,"style":8734},[172],[50,9172,8760],{"className":9173},[182],[50,9175],{"className":9176,"style":736},[244],[50,9178,7714],{"className":9179},[1212],[50,9181],{"className":9182,"style":736},[244],[50,9184,9186,9189,9192,9195,9198],{"className":9185},[168],[50,9187],{"className":9188,"style":8734},[172],[50,9190,9091],{"className":9191},[182],[50,9193],{"className":9194,"style":699},[244],[50,9196,1069],{"className":9197},[703],[50,9199],{"className":9200,"style":699},[244],[50,9202,9204,9208,9211,9214,9217],{"className":9203},[168],[50,9205],{"className":9206,"style":9207},[172],"height:0.8389em;vertical-align:-0.1944em;",[50,9209,9096],{"className":9210},[182],[50,9212,90],{"className":9213},[240],[50,9215],{"className":9216,"style":245},[244],[50,9218,9101],{"className":9219},[182],[11,9221,9222,9223,9226],{},"Our calculations have yielded the best coefficients that ",[747,9224,9225],{},"minimize"," the mean squared error (but do not eliminate it completely). To measure the error of our model, we can calculate the MSE using the formula mentioned earlier:",[50,9228,9230],{"className":9229},[650],[50,9231,9233,9298],{"className":9232},[53],[50,9234,9236],{"className":9235},[57],[59,9237,9238],{"xmlns":61,"display":659},[63,9239,9240,9296],{},[66,9241,9242,9244,9246,9248,9250,9256,9270,9272,9278,9280,9290],{},[80,9243,3182],{},[80,9245,3185],{},[80,9247,3188],{},[69,9249,1069],{},[3192,9251,9252,9254],{},[84,9253,86],{},[80,9255,142],{},[3199,9257,9258,9260,9268],{},[69,9259,3203],{},[66,9261,9262,9264,9266],{},[80,9263,519],{},[69,9265,1069],{},[84,9267,86],{},[80,9269,142],{},[69,9271,75],{"stretchy":71},[77,9273,9274,9276],{},[80,9275,95],{},[80,9277,519],{},[69,9279,2587],{},[2589,9281,9282,9288],{"accent":89},[77,9283,9284,9286],{},[80,9285,95],{},[80,9287,519],{},[69,9289,2599],{},[3235,9291,9292,9294],{},[69,9293,100],{"stretchy":71},[84,9295,111],{},[157,9297,3243],{"encoding":159},[50,9299,9301,9325,9515],{"className":9300,"ariaHidden":89},[164],[50,9302,9304,9307,9310,9313,9316,9319,9322],{"className":9303},[168],[50,9305],{"className":9306,"style":713},[172],[50,9308,3182],{"className":9309,"style":3256},[182,186],[50,9311,3185],{"className":9312,"style":3260},[182,186],[50,9314,3188],{"className":9315,"style":3260},[182,186],[50,9317],{"className":9318,"style":699},[244],[50,9320,1069],{"className":9321},[703],[50,9323],{"className":9324,"style":699},[244],[50,9326,9328,9331,9393,9396,9463,9466,9506,9509,9512],{"className":9327},[168],[50,9329],{"className":9330,"style":3279},[172],[50,9332,9334,9337,9390],{"className":9333},[182],[50,9335],{"className":9336},[177,3286],[50,9338,9340],{"className":9339},[3192],[50,9341,9343,9382],{"className":9342},[194,195],[50,9344,9346,9379],{"className":9345},[199],[50,9347,9349,9360,9368],{"className":9348,"style":3299},[203],[50,9350,9351,9354],{"style":3302},[50,9352],{"className":9353,"style":2754},[211],[50,9355,9357],{"className":9356},[182],[50,9358,142],{"className":9359},[182,186],[50,9361,9362,9365],{"style":3314},[50,9363],{"className":9364,"style":2754},[211],[50,9366],{"className":9367,"style":3322},[3321],[50,9369,9370,9373],{"style":3325},[50,9371],{"className":9372,"style":2754},[211],[50,9374,9376],{"className":9375},[182],[50,9377,86],{"className":9378},[182],[50,9380,227],{"className":9381},[226],[50,9383,9385],{"className":9384},[199],[50,9386,9388],{"className":9387,"style":3344},[203],[50,9389],{},[50,9391],{"className":9392},[291,3286],[50,9394],{"className":9395,"style":245},[244],[50,9397,9399],{"className":9398},[3356,3357],[50,9400,9402,9455],{"className":9401},[194,195],[50,9403,9405,9452],{"className":9404},[199],[50,9406,9408,9428,9438],{"className":9407,"style":3367},[203],[50,9409,9410,9413],{"style":3370},[50,9411],{"className":9412,"style":3374},[211],[50,9414,9416],{"className":9415},[216,217,218,219],[50,9417,9419,9422,9425],{"className":9418},[182,219],[50,9420,519],{"className":9421},[182,186,219],[50,9423,1069],{"className":9424},[703,219],[50,9426,86],{"className":9427},[182,219],[50,9429,9430,9433],{"style":3392},[50,9431],{"className":9432,"style":3374},[211],[50,9434,9435],{},[50,9436,3203],{"className":9437},[3356,3401,3402],[50,9439,9440,9443],{"style":3405},[50,9441],{"className":9442,"style":3374},[211],[50,9444,9446],{"className":9445},[216,217,218,219],[50,9447,9449],{"className":9448},[182,219],[50,9450,142],{"className":9451},[182,186,219],[50,9453,227],{"className":9454},[226],[50,9456,9458],{"className":9457},[199],[50,9459,9461],{"className":9460,"style":3427},[203],[50,9462],{},[50,9464,75],{"className":9465},[177],[50,9467,9469,9472],{"className":9468},[182],[50,9470,95],{"className":9471,"style":252},[182,186],[50,9473,9475],{"className":9474},[190],[50,9476,9478,9498],{"className":9477},[194,195],[50,9479,9481,9495],{"className":9480},[199],[50,9482,9484],{"className":9483,"style":551},[203],[50,9485,9486,9489],{"style":267},[50,9487],{"className":9488,"style":212},[211],[50,9490,9492],{"className":9491},[216,217,218,219],[50,9493,519],{"className":9494},[182,186,219],[50,9496,227],{"className":9497},[226],[50,9499,9501],{"className":9500},[199],[50,9502,9504],{"className":9503,"style":234},[203],[50,9505],{},[50,9507],{"className":9508,"style":736},[244],[50,9510,2587],{"className":9511},[1212],[50,9513],{"className":9514,"style":736},[244],[50,9516,9518,9521,9600],{"className":9517},[168],[50,9519],{"className":9520,"style":3488},[172],[50,9522,9524],{"className":9523},[182,2737],[50,9525,9527,9592],{"className":9526},[194,195],[50,9528,9530,9589],{"className":9529},[199],[50,9531,9533,9578],{"className":9532,"style":3501},[203],[50,9534,9535,9538],{"style":2750},[50,9536],{"className":9537,"style":2754},[211],[50,9539,9541,9544],{"className":9540},[182],[50,9542,95],{"className":9543,"style":252},[182,186],[50,9545,9547],{"className":9546},[190],[50,9548,9550,9570],{"className":9549},[194,195],[50,9551,9553,9567],{"className":9552},[199],[50,9554,9556],{"className":9555,"style":551},[203],[50,9557,9558,9561],{"style":267},[50,9559],{"className":9560,"style":212},[211],[50,9562,9564],{"className":9563},[216,217,218,219],[50,9565,519],{"className":9566},[182,186,219],[50,9568,227],{"className":9569},[226],[50,9571,9573],{"className":9572},[199],[50,9574,9576],{"className":9575,"style":234},[203],[50,9577],{},[50,9579,9580,9583],{"style":2750},[50,9581],{"className":9582,"style":2754},[211],[50,9584,9586],{"className":9585,"style":2805},[2804],[50,9587,2599],{"className":9588},[182],[50,9590,227],{"className":9591},[226],[50,9593,9595],{"className":9594},[199],[50,9596,9598],{"className":9597,"style":2818},[203],[50,9599],{},[50,9601,9603,9606],{"className":9602},[291],[50,9604,100],{"className":9605},[291],[50,9607,9609],{"className":9608},[190],[50,9610,9612],{"className":9611},[194],[50,9613,9615],{"className":9614},[199],[50,9616,9618],{"className":9617,"style":3587},[203],[50,9619,9620,9623],{"style":3590},[50,9621],{"className":9622,"style":212},[211],[50,9624,9626],{"className":9625},[216,217,218,219],[50,9627,111],{"className":9628},[182,219],[11,9630,1534],{},[741,9632,9633,9663,9735],{},[744,9634,9635,7399],{},[50,9636,9638,9651],{"className":9637},[53],[50,9639,9641],{"className":9640},[57],[59,9642,9643],{"xmlns":61},[63,9644,9645,9649],{},[66,9646,9647],{},[80,9648,142],{},[157,9650,142],{"encoding":159},[50,9652,9654],{"className":9653,"ariaHidden":89},[164],[50,9655,9657,9660],{"className":9656},[168],[50,9658],{"className":9659,"style":1528},[172],[50,9661,142],{"className":9662},[182,186],[744,9664,9665,9734],{},[50,9666,9668,9685],{"className":9667},[53],[50,9669,9671],{"className":9670},[57],[59,9672,9673],{"xmlns":61},[63,9674,9675,9683],{},[66,9676,9677],{},[77,9678,9679,9681],{},[80,9680,95],{},[80,9682,519],{},[157,9684,595],{"encoding":159},[50,9686,9688],{"className":9687,"ariaHidden":89},[164],[50,9689,9691,9694],{"className":9690},[168],[50,9692],{"className":9693,"style":605},[172],[50,9695,9697,9700],{"className":9696},[182],[50,9698,95],{"className":9699,"style":252},[182,186],[50,9701,9703],{"className":9702},[190],[50,9704,9706,9726],{"className":9705},[194,195],[50,9707,9709,9723],{"className":9708},[199],[50,9710,9712],{"className":9711,"style":551},[203],[50,9713,9714,9717],{"style":267},[50,9715],{"className":9716,"style":212},[211],[50,9718,9720],{"className":9719},[216,217,218,219],[50,9721,519],{"className":9722},[182,186,219],[50,9724,227],{"className":9725},[226],[50,9727,9729],{"className":9728},[199],[50,9730,9732],{"className":9731,"style":234},[203],[50,9733],{}," are the real values of price for each example.",[744,9736,9737,9849],{},[50,9738,9740,9761],{"className":9739},[53],[50,9741,9743],{"className":9742},[57],[59,9744,9745],{"xmlns":61},[63,9746,9747,9759],{},[66,9748,9749],{},[77,9750,9751,9757],{},[2589,9752,9753,9755],{"accent":89},[80,9754,95],{},[69,9756,2599],{},[80,9758,519],{},[157,9760,3762],{"encoding":159},[50,9762,9764],{"className":9763,"ariaHidden":89},[164],[50,9765,9767,9770],{"className":9766},[168],[50,9768],{"className":9769,"style":691},[172],[50,9771,9773,9815],{"className":9772},[182],[50,9774,9776],{"className":9775},[182,2737],[50,9777,9779,9807],{"className":9778},[194,195],[50,9780,9782,9804],{"className":9781},[199],[50,9783,9785,9793],{"className":9784,"style":3501},[203],[50,9786,9787,9790],{"style":2750},[50,9788],{"className":9789,"style":2754},[211],[50,9791,95],{"className":9792,"style":252},[182,186],[50,9794,9795,9798],{"style":2750},[50,9796],{"className":9797,"style":2754},[211],[50,9799,9801],{"className":9800,"style":3803},[2804],[50,9802,2599],{"className":9803},[182],[50,9805,227],{"className":9806},[226],[50,9808,9810],{"className":9809},[199],[50,9811,9813],{"className":9812,"style":2818},[203],[50,9814],{},[50,9816,9818],{"className":9817},[190],[50,9819,9821,9841],{"className":9820},[194,195],[50,9822,9824,9838],{"className":9823},[199],[50,9825,9827],{"className":9826,"style":551},[203],[50,9828,9829,9832],{"style":267},[50,9830],{"className":9831,"style":212},[211],[50,9833,9835],{"className":9834},[216,217,218,219],[50,9836,519],{"className":9837},[182,186,219],[50,9839,227],{"className":9840},[226],[50,9842,9844],{"className":9843},[199],[50,9845,9847],{"className":9846,"style":234},[203],[50,9848],{}," are the model predictions for each example, which are calculated using the linear regression model.",[996,9851,9852],{},[744,9853,9854],{},"We calculate the predictions for each example:",[741,9856,9857,10052,10248],{},[744,9858,9859,9860],{},"For 50 m²: ",[50,9861,9863,9901],{"className":9862},[53],[50,9864,9866],{"className":9865},[57],[59,9867,9868],{"xmlns":61},[63,9869,9870,9898],{},[66,9871,9872,9882,9884,9886,9888,9890,9892,9894,9896],{},[2589,9873,9874,9880],{"accent":89},[77,9875,9876,9878],{},[80,9877,95],{},[84,9879,86],{},[69,9881,2599],{},[69,9883,1069],{},[84,9885,8760],{},[69,9887,7714],{},[84,9889,5958],{},[69,9891,1069],{},[84,9893,5966],{},[69,9895,90],{"separator":89},[84,9897,9101],{},[157,9899,9900],{"encoding":159},"\\hat{y_1} = 2000 * 50 = 100,000",[50,9902,9904,9998,10016,10034],{"className":9903,"ariaHidden":89},[164],[50,9905,9907,9910,9989,9992,9995],{"className":9906},[168],[50,9908],{"className":9909,"style":691},[172],[50,9911,9913],{"className":9912},[182,2737],[50,9914,9916,9981],{"className":9915},[194,195],[50,9917,9919,9978],{"className":9918},[199],[50,9920,9922,9967],{"className":9921,"style":3501},[203],[50,9923,9924,9927],{"style":2750},[50,9925],{"className":9926,"style":2754},[211],[50,9928,9930,9933],{"className":9929},[182],[50,9931,95],{"className":9932,"style":252},[182,186],[50,9934,9936],{"className":9935},[190],[50,9937,9939,9959],{"className":9938},[194,195],[50,9940,9942,9956],{"className":9941},[199],[50,9943,9945],{"className":9944,"style":204},[203],[50,9946,9947,9950],{"style":267},[50,9948],{"className":9949,"style":212},[211],[50,9951,9953],{"className":9952},[216,217,218,219],[50,9954,86],{"className":9955},[182,219],[50,9957,227],{"className":9958},[226],[50,9960,9962],{"className":9961},[199],[50,9963,9965],{"className":9964,"style":234},[203],[50,9966],{},[50,9968,9969,9972],{"style":2750},[50,9970],{"className":9971,"style":2754},[211],[50,9973,9975],{"className":9974,"style":2805},[2804],[50,9976,2599],{"className":9977},[182],[50,9979,227],{"className":9980},[226],[50,9982,9984],{"className":9983},[199],[50,9985,9987],{"className":9986,"style":2818},[203],[50,9988],{},[50,9990],{"className":9991,"style":699},[244],[50,9993,1069],{"className":9994},[703],[50,9996],{"className":9997,"style":699},[244],[50,9999,10001,10004,10007,10010,10013],{"className":10000},[168],[50,10002],{"className":10003,"style":8734},[172],[50,10005,8760],{"className":10006},[182],[50,10008],{"className":10009,"style":736},[244],[50,10011,7714],{"className":10012},[1212],[50,10014],{"className":10015,"style":736},[244],[50,10017,10019,10022,10025,10028,10031],{"className":10018},[168],[50,10020],{"className":10021,"style":8734},[172],[50,10023,5958],{"className":10024},[182],[50,10026],{"className":10027,"style":699},[244],[50,10029,1069],{"className":10030},[703],[50,10032],{"className":10033,"style":699},[244],[50,10035,10037,10040,10043,10046,10049],{"className":10036},[168],[50,10038],{"className":10039,"style":9207},[172],[50,10041,5966],{"className":10042},[182],[50,10044,90],{"className":10045},[240],[50,10047],{"className":10048,"style":245},[244],[50,10050,9101],{"className":10051},[182],[744,10053,10054,10055],{},"For 100 m²: ",[50,10056,10058,10097],{"className":10057},[53],[50,10059,10061],{"className":10060},[57],[59,10062,10063],{"xmlns":61},[63,10064,10065,10094],{},[66,10066,10067,10077,10079,10081,10083,10085,10087,10090,10092],{},[2589,10068,10069,10075],{"accent":89},[77,10070,10071,10073],{},[80,10072,95],{},[84,10074,111],{},[69,10076,2599],{},[69,10078,1069],{},[84,10080,8760],{},[69,10082,7714],{},[84,10084,5966],{},[69,10086,1069],{},[84,10088,10089],{},"200",[69,10091,90],{"separator":89},[84,10093,9101],{},[157,10095,10096],{"encoding":159},"\\hat{y_2} = 2000 * 100 = 200,000",[50,10098,10100,10194,10212,10230],{"className":10099,"ariaHidden":89},[164],[50,10101,10103,10106,10185,10188,10191],{"className":10102},[168],[50,10104],{"className":10105,"style":691},[172],[50,10107,10109],{"className":10108},[182,2737],[50,10110,10112,10177],{"className":10111},[194,195],[50,10113,10115,10174],{"className":10114},[199],[50,10116,10118,10163],{"className":10117,"style":3501},[203],[50,10119,10120,10123],{"style":2750},[50,10121],{"className":10122,"style":2754},[211],[50,10124,10126,10129],{"className":10125},[182],[50,10127,95],{"className":10128,"style":252},[182,186],[50,10130,10132],{"className":10131},[190],[50,10133,10135,10155],{"className":10134},[194,195],[50,10136,10138,10152],{"className":10137},[199],[50,10139,10141],{"className":10140,"style":204},[203],[50,10142,10143,10146],{"style":267},[50,10144],{"className":10145,"style":212},[211],[50,10147,10149],{"className":10148},[216,217,218,219],[50,10150,111],{"className":10151},[182,219],[50,10153,227],{"className":10154},[226],[50,10156,10158],{"className":10157},[199],[50,10159,10161],{"className":10160,"style":234},[203],[50,10162],{},[50,10164,10165,10168],{"style":2750},[50,10166],{"className":10167,"style":2754},[211],[50,10169,10171],{"className":10170,"style":2805},[2804],[50,10172,2599],{"className":10173},[182],[50,10175,227],{"className":10176},[226],[50,10178,10180],{"className":10179},[199],[50,10181,10183],{"className":10182,"style":2818},[203],[50,10184],{},[50,10186],{"className":10187,"style":699},[244],[50,10189,1069],{"className":10190},[703],[50,10192],{"className":10193,"style":699},[244],[50,10195,10197,10200,10203,10206,10209],{"className":10196},[168],[50,10198],{"className":10199,"style":8734},[172],[50,10201,8760],{"className":10202},[182],[50,10204],{"className":10205,"style":736},[244],[50,10207,7714],{"className":10208},[1212],[50,10210],{"className":10211,"style":736},[244],[50,10213,10215,10218,10221,10224,10227],{"className":10214},[168],[50,10216],{"className":10217,"style":8734},[172],[50,10219,5966],{"className":10220},[182],[50,10222],{"className":10223,"style":699},[244],[50,10225,1069],{"className":10226},[703],[50,10228],{"className":10229,"style":699},[244],[50,10231,10233,10236,10239,10242,10245],{"className":10232},[168],[50,10234],{"className":10235,"style":9207},[172],[50,10237,10089],{"className":10238},[182],[50,10240,90],{"className":10241},[240],[50,10243],{"className":10244,"style":245},[244],[50,10246,9101],{"className":10247},[182],[744,10249,10250,10251],{},"For 150 m²: ",[50,10252,10254,10293],{"className":10253},[53],[50,10255,10257],{"className":10256},[57],[59,10258,10259],{"xmlns":61},[63,10260,10261,10290],{},[66,10262,10263,10273,10275,10277,10279,10281,10283,10286,10288],{},[2589,10264,10265,10271],{"accent":89},[77,10266,10267,10269],{},[80,10268,95],{},[84,10270,7707],{},[69,10272,2599],{},[69,10274,1069],{},[84,10276,8760],{},[69,10278,7714],{},[84,10280,5974],{},[69,10282,1069],{},[84,10284,10285],{},"300",[69,10287,90],{"separator":89},[84,10289,9101],{},[157,10291,10292],{"encoding":159},"\\hat{y_3} = 2000 * 150 = 300,000",[50,10294,10296,10390,10408,10426],{"className":10295,"ariaHidden":89},[164],[50,10297,10299,10302,10381,10384,10387],{"className":10298},[168],[50,10300],{"className":10301,"style":691},[172],[50,10303,10305],{"className":10304},[182,2737],[50,10306,10308,10373],{"className":10307},[194,195],[50,10309,10311,10370],{"className":10310},[199],[50,10312,10314,10359],{"className":10313,"style":3501},[203],[50,10315,10316,10319],{"style":2750},[50,10317],{"className":10318,"style":2754},[211],[50,10320,10322,10325],{"className":10321},[182],[50,10323,95],{"className":10324,"style":252},[182,186],[50,10326,10328],{"className":10327},[190],[50,10329,10331,10351],{"className":10330},[194,195],[50,10332,10334,10348],{"className":10333},[199],[50,10335,10337],{"className":10336,"style":204},[203],[50,10338,10339,10342],{"style":267},[50,10340],{"className":10341,"style":212},[211],[50,10343,10345],{"className":10344},[216,217,218,219],[50,10346,7707],{"className":10347},[182,219],[50,10349,227],{"className":10350},[226],[50,10352,10354],{"className":10353},[199],[50,10355,10357],{"className":10356,"style":234},[203],[50,10358],{},[50,10360,10361,10364],{"style":2750},[50,10362],{"className":10363,"style":2754},[211],[50,10365,10367],{"className":10366,"style":2805},[2804],[50,10368,2599],{"className":10369},[182],[50,10371,227],{"className":10372},[226],[50,10374,10376],{"className":10375},[199],[50,10377,10379],{"className":10378,"style":2818},[203],[50,10380],{},[50,10382],{"className":10383,"style":699},[244],[50,10385,1069],{"className":10386},[703],[50,10388],{"className":10389,"style":699},[244],[50,10391,10393,10396,10399,10402,10405],{"className":10392},[168],[50,10394],{"className":10395,"style":8734},[172],[50,10397,8760],{"className":10398},[182],[50,10400],{"className":10401,"style":736},[244],[50,10403,7714],{"className":10404},[1212],[50,10406],{"className":10407,"style":736},[244],[50,10409,10411,10414,10417,10420,10423],{"className":10410},[168],[50,10412],{"className":10413,"style":8734},[172],[50,10415,5974],{"className":10416},[182],[50,10418],{"className":10419,"style":699},[244],[50,10421,1069],{"className":10422},[703],[50,10424],{"className":10425,"style":699},[244],[50,10427,10429,10432,10435,10438,10441],{"className":10428},[168],[50,10430],{"className":10431,"style":9207},[172],[50,10433,10285],{"className":10434},[182],[50,10436,90],{"className":10437},[240],[50,10439],{"className":10440,"style":245},[244],[50,10442,9101],{"className":10443},[182],[996,10445,10447],{"start":10446},2,[744,10448,10449],{},"We calculate the errors for each example:",[50,10451,10453],{"className":10452},[650],[50,10454,10456,10495],{"className":10455},[53],[50,10457,10459],{"className":10458},[57],[59,10460,10461],{"xmlns":61,"display":659},[63,10462,10463,10492],{},[66,10464,10465,10472,10474,10480,10482],{},[77,10466,10467,10470],{},[80,10468,10469],{},"e",[80,10471,519],{},[69,10473,1069],{},[77,10475,10476,10478],{},[80,10477,95],{},[80,10479,519],{},[69,10481,2587],{},[2589,10483,10484,10490],{"accent":89},[77,10485,10486,10488],{},[80,10487,95],{},[80,10489,519],{},[69,10491,2599],{},[157,10493,10494],{"encoding":159},"e_i = y_i - \\hat{y_i}",[50,10496,10498,10553,10608],{"className":10497,"ariaHidden":89},[164],[50,10499,10501,10504,10544,10547,10550],{"className":10500},[168],[50,10502],{"className":10503,"style":532},[172],[50,10505,10507,10510],{"className":10506},[182],[50,10508,10469],{"className":10509},[182,186],[50,10511,10513],{"className":10512},[190],[50,10514,10516,10536],{"className":10515},[194,195],[50,10517,10519,10533],{"className":10518},[199],[50,10520,10522],{"className":10521,"style":551},[203],[50,10523,10524,10527],{"style":207},[50,10525],{"className":10526,"style":212},[211],[50,10528,10530],{"className":10529},[216,217,218,219],[50,10531,519],{"className":10532},[182,186,219],[50,10534,227],{"className":10535},[226],[50,10537,10539],{"className":10538},[199],[50,10540,10542],{"className":10541,"style":234},[203],[50,10543],{},[50,10545],{"className":10546,"style":699},[244],[50,10548,1069],{"className":10549},[703],[50,10551],{"className":10552,"style":699},[244],[50,10554,10556,10559,10599,10602,10605],{"className":10555},[168],[50,10557],{"className":10558,"style":2675},[172],[50,10560,10562,10565],{"className":10561},[182],[50,10563,95],{"className":10564,"style":252},[182,186],[50,10566,10568],{"className":10567},[190],[50,10569,10571,10591],{"className":10570},[194,195],[50,10572,10574,10588],{"className":10573},[199],[50,10575,10577],{"className":10576,"style":551},[203],[50,10578,10579,10582],{"style":267},[50,10580],{"className":10581,"style":212},[211],[50,10583,10585],{"className":10584},[216,217,218,219],[50,10586,519],{"className":10587},[182,186,219],[50,10589,227],{"className":10590},[226],[50,10592,10594],{"className":10593},[199],[50,10595,10597],{"className":10596,"style":234},[203],[50,10598],{},[50,10600],{"className":10601,"style":736},[244],[50,10603,2587],{"className":10604},[1212],[50,10606],{"className":10607,"style":736},[244],[50,10609,10611,10614],{"className":10610},[168],[50,10612],{"className":10613,"style":691},[172],[50,10615,10617],{"className":10616},[182,2737],[50,10618,10620,10685],{"className":10619},[194,195],[50,10621,10623,10682],{"className":10622},[199],[50,10624,10626,10671],{"className":10625,"style":3501},[203],[50,10627,10628,10631],{"style":2750},[50,10629],{"className":10630,"style":2754},[211],[50,10632,10634,10637],{"className":10633},[182],[50,10635,95],{"className":10636,"style":252},[182,186],[50,10638,10640],{"className":10639},[190],[50,10641,10643,10663],{"className":10642},[194,195],[50,10644,10646,10660],{"className":10645},[199],[50,10647,10649],{"className":10648,"style":551},[203],[50,10650,10651,10654],{"style":267},[50,10652],{"className":10653,"style":212},[211],[50,10655,10657],{"className":10656},[216,217,218,219],[50,10658,519],{"className":10659},[182,186,219],[50,10661,227],{"className":10662},[226],[50,10664,10666],{"className":10665},[199],[50,10667,10669],{"className":10668,"style":234},[203],[50,10670],{},[50,10672,10673,10676],{"style":2750},[50,10674],{"className":10675,"style":2754},[211],[50,10677,10679],{"className":10678,"style":2805},[2804],[50,10680,2599],{"className":10681},[182],[50,10683,227],{"className":10684},[226],[50,10686,10688],{"className":10687},[199],[50,10689,10691],{"className":10690,"style":2818},[203],[50,10692],{},[741,10694,10695,10834,10973],{},[744,10696,10697,10698],{},"Error for 50 m²: ",[50,10699,10701,10731],{"className":10700},[53],[50,10702,10704],{"className":10703},[57],[59,10705,10706],{"xmlns":61},[63,10707,10708,10728],{},[66,10709,10710,10716,10718,10720,10722,10724,10726],{},[77,10711,10712,10714],{},[80,10713,10469],{},[84,10715,86],{},[69,10717,1069],{},[84,10719,7717],{},[69,10721,2587],{},[84,10723,7717],{},[69,10725,1069],{},[84,10727,1077],{},[157,10729,10730],{"encoding":159},"e_1 = 100000 - 100000 = 0",[50,10732,10734,10789,10807,10825],{"className":10733,"ariaHidden":89},[164],[50,10735,10737,10740,10780,10783,10786],{"className":10736},[168],[50,10738],{"className":10739,"style":532},[172],[50,10741,10743,10746],{"className":10742},[182],[50,10744,10469],{"className":10745},[182,186],[50,10747,10749],{"className":10748},[190],[50,10750,10752,10772],{"className":10751},[194,195],[50,10753,10755,10769],{"className":10754},[199],[50,10756,10758],{"className":10757,"style":204},[203],[50,10759,10760,10763],{"style":207},[50,10761],{"className":10762,"style":212},[211],[50,10764,10766],{"className":10765},[216,217,218,219],[50,10767,86],{"className":10768},[182,219],[50,10770,227],{"className":10771},[226],[50,10773,10775],{"className":10774},[199],[50,10776,10778],{"className":10777,"style":234},[203],[50,10779],{},[50,10781],{"className":10782,"style":699},[244],[50,10784,1069],{"className":10785},[703],[50,10787],{"className":10788,"style":699},[244],[50,10790,10792,10795,10798,10801,10804],{"className":10791},[168],[50,10793],{"className":10794,"style":8932},[172],[50,10796,7717],{"className":10797},[182],[50,10799],{"className":10800,"style":736},[244],[50,10802,2587],{"className":10803},[1212],[50,10805],{"className":10806,"style":736},[244],[50,10808,10810,10813,10816,10819,10822],{"className":10809},[168],[50,10811],{"className":10812,"style":8734},[172],[50,10814,7717],{"className":10815},[182],[50,10817],{"className":10818,"style":699},[244],[50,10820,1069],{"className":10821},[703],[50,10823],{"className":10824,"style":699},[244],[50,10826,10828,10831],{"className":10827},[168],[50,10829],{"className":10830,"style":8734},[172],[50,10832,1077],{"className":10833},[182],[744,10835,10836,10837],{},"Error for 100 m²: ",[50,10838,10840,10870],{"className":10839},[53],[50,10841,10843],{"className":10842},[57],[59,10844,10845],{"xmlns":61},[63,10846,10847,10867],{},[66,10848,10849,10855,10857,10859,10861,10863,10865],{},[77,10850,10851,10853],{},[80,10852,10469],{},[84,10854,111],{},[69,10856,1069],{},[84,10858,7726],{},[69,10860,2587],{},[84,10862,7726],{},[69,10864,1069],{},[84,10866,1077],{},[157,10868,10869],{"encoding":159},"e_2 = 200000 - 200000 = 0",[50,10871,10873,10928,10946,10964],{"className":10872,"ariaHidden":89},[164],[50,10874,10876,10879,10919,10922,10925],{"className":10875},[168],[50,10877],{"className":10878,"style":532},[172],[50,10880,10882,10885],{"className":10881},[182],[50,10883,10469],{"className":10884},[182,186],[50,10886,10888],{"className":10887},[190],[50,10889,10891,10911],{"className":10890},[194,195],[50,10892,10894,10908],{"className":10893},[199],[50,10895,10897],{"className":10896,"style":204},[203],[50,10898,10899,10902],{"style":207},[50,10900],{"className":10901,"style":212},[211],[50,10903,10905],{"className":10904},[216,217,218,219],[50,10906,111],{"className":10907},[182,219],[50,10909,227],{"className":10910},[226],[50,10912,10914],{"className":10913},[199],[50,10915,10917],{"className":10916,"style":234},[203],[50,10918],{},[50,10920],{"className":10921,"style":699},[244],[50,10923,1069],{"className":10924},[703],[50,10926],{"className":10927,"style":699},[244],[50,10929,10931,10934,10937,10940,10943],{"className":10930},[168],[50,10932],{"className":10933,"style":8932},[172],[50,10935,7726],{"className":10936},[182],[50,10938],{"className":10939,"style":736},[244],[50,10941,2587],{"className":10942},[1212],[50,10944],{"className":10945,"style":736},[244],[50,10947,10949,10952,10955,10958,10961],{"className":10948},[168],[50,10950],{"className":10951,"style":8734},[172],[50,10953,7726],{"className":10954},[182],[50,10956],{"className":10957,"style":699},[244],[50,10959,1069],{"className":10960},[703],[50,10962],{"className":10963,"style":699},[244],[50,10965,10967,10970],{"className":10966},[168],[50,10968],{"className":10969,"style":8734},[172],[50,10971,1077],{"className":10972},[182],[744,10974,10975,10976],{},"Error for 150 m²: ",[50,10977,10979,11009],{"className":10978},[53],[50,10980,10982],{"className":10981},[57],[59,10983,10984],{"xmlns":61},[63,10985,10986,11006],{},[66,10987,10988,10994,10996,10998,11000,11002,11004],{},[77,10989,10990,10992],{},[80,10991,10469],{},[84,10993,7707],{},[69,10995,1069],{},[84,10997,7735],{},[69,10999,2587],{},[84,11001,7735],{},[69,11003,1069],{},[84,11005,1077],{},[157,11007,11008],{"encoding":159},"e_3 = 300000 - 300000 = 0",[50,11010,11012,11067,11085,11103],{"className":11011,"ariaHidden":89},[164],[50,11013,11015,11018,11058,11061,11064],{"className":11014},[168],[50,11016],{"className":11017,"style":532},[172],[50,11019,11021,11024],{"className":11020},[182],[50,11022,10469],{"className":11023},[182,186],[50,11025,11027],{"className":11026},[190],[50,11028,11030,11050],{"className":11029},[194,195],[50,11031,11033,11047],{"className":11032},[199],[50,11034,11036],{"className":11035,"style":204},[203],[50,11037,11038,11041],{"style":207},[50,11039],{"className":11040,"style":212},[211],[50,11042,11044],{"className":11043},[216,217,218,219],[50,11045,7707],{"className":11046},[182,219],[50,11048,227],{"className":11049},[226],[50,11051,11053],{"className":11052},[199],[50,11054,11056],{"className":11055,"style":234},[203],[50,11057],{},[50,11059],{"className":11060,"style":699},[244],[50,11062,1069],{"className":11063},[703],[50,11065],{"className":11066,"style":699},[244],[50,11068,11070,11073,11076,11079,11082],{"className":11069},[168],[50,11071],{"className":11072,"style":8932},[172],[50,11074,7735],{"className":11075},[182],[50,11077],{"className":11078,"style":736},[244],[50,11080,2587],{"className":11081},[1212],[50,11083],{"className":11084,"style":736},[244],[50,11086,11088,11091,11094,11097,11100],{"className":11087},[168],[50,11089],{"className":11090,"style":8734},[172],[50,11092,7735],{"className":11093},[182],[50,11095],{"className":11096,"style":699},[244],[50,11098,1069],{"className":11099},[703],[50,11101],{"className":11102,"style":699},[244],[50,11104,11106,11109],{"className":11105},[168],[50,11107],{"className":11108,"style":8734},[172],[50,11110,1077],{"className":11111},[182],[996,11113,11115],{"start":11114},3,[744,11116,11117],{},"We square the errors and average them to obtain the MSE:",[50,11119,11121],{"className":11120},[650],[50,11122,11124,11180],{"className":11123},[53],[50,11125,11127],{"className":11126},[57],[59,11128,11129],{"xmlns":61,"display":659},[63,11130,11131,11177],{},[66,11132,11133,11135,11137,11139,11141,11147,11149,11155,11157,11163,11165,11171,11173,11175],{},[80,11134,3182],{},[80,11136,3185],{},[80,11138,3188],{},[69,11140,1069],{},[3192,11142,11143,11145],{},[84,11144,86],{},[84,11146,7707],{},[69,11148,75],{"stretchy":71},[3235,11150,11151,11153],{},[84,11152,1077],{},[84,11154,111],{},[69,11156,1080],{},[3235,11158,11159,11161],{},[84,11160,1077],{},[84,11162,111],{},[69,11164,1080],{},[3235,11166,11167,11169],{},[84,11168,1077],{},[84,11170,111],{},[69,11172,100],{"stretchy":71},[69,11174,1069],{},[84,11176,1077],{},[157,11178,11179],{"encoding":159},"MSE = \\frac{1}{3} (0^2 + 0^2 + 0^2) = 0",[50,11181,11183,11207,11316,11361,11408],{"className":11182,"ariaHidden":89},[164],[50,11184,11186,11189,11192,11195,11198,11201,11204],{"className":11185},[168],[50,11187],{"className":11188,"style":713},[172],[50,11190,3182],{"className":11191,"style":3256},[182,186],[50,11193,3185],{"className":11194,"style":3260},[182,186],[50,11196,3188],{"className":11197,"style":3260},[182,186],[50,11199],{"className":11200,"style":699},[244],[50,11202,1069],{"className":11203},[703],[50,11205],{"className":11206,"style":699},[244],[50,11208,11210,11213,11275,11278,11307,11310,11313],{"className":11209},[168],[50,11211],{"className":11212,"style":8414},[172],[50,11214,11216,11219,11272],{"className":11215},[182],[50,11217],{"className":11218},[177,3286],[50,11220,11222],{"className":11221},[3192],[50,11223,11225,11264],{"className":11224},[194,195],[50,11226,11228,11261],{"className":11227},[199],[50,11229,11231,11242,11250],{"className":11230,"style":3299},[203],[50,11232,11233,11236],{"style":3302},[50,11234],{"className":11235,"style":2754},[211],[50,11237,11239],{"className":11238},[182],[50,11240,7707],{"className":11241},[182],[50,11243,11244,11247],{"style":3314},[50,11245],{"className":11246,"style":2754},[211],[50,11248],{"className":11249,"style":3322},[3321],[50,11251,11252,11255],{"style":3325},[50,11253],{"className":11254,"style":2754},[211],[50,11256,11258],{"className":11257},[182],[50,11259,86],{"className":11260},[182],[50,11262,227],{"className":11263},[226],[50,11265,11267],{"className":11266},[199],[50,11268,11270],{"className":11269,"style":3344},[203],[50,11271],{},[50,11273],{"className":11274},[291,3286],[50,11276,75],{"className":11277},[177],[50,11279,11281,11284],{"className":11280},[182],[50,11282,1077],{"className":11283},[182],[50,11285,11287],{"className":11286},[190],[50,11288,11290],{"className":11289},[194],[50,11291,11293],{"className":11292},[199],[50,11294,11296],{"className":11295,"style":3587},[203],[50,11297,11298,11301],{"style":3590},[50,11299],{"className":11300,"style":212},[211],[50,11302,11304],{"className":11303},[216,217,218,219],[50,11305,111],{"className":11306},[182,219],[50,11308],{"className":11309,"style":736},[244],[50,11311,1080],{"className":11312},[1212],[50,11314],{"className":11315,"style":736},[244],[50,11317,11319,11323,11352,11355,11358],{"className":11318},[168],[50,11320],{"className":11321,"style":11322},[172],"height:0.9474em;vertical-align:-0.0833em;",[50,11324,11326,11329],{"className":11325},[182],[50,11327,1077],{"className":11328},[182],[50,11330,11332],{"className":11331},[190],[50,11333,11335],{"className":11334},[194],[50,11336,11338],{"className":11337},[199],[50,11339,11341],{"className":11340,"style":3587},[203],[50,11342,11343,11346],{"style":3590},[50,11344],{"className":11345,"style":212},[211],[50,11347,11349],{"className":11348},[216,217,218,219],[50,11350,111],{"className":11351},[182,219],[50,11353],{"className":11354,"style":736},[244],[50,11356,1080],{"className":11357},[1212],[50,11359],{"className":11360,"style":736},[244],[50,11362,11364,11367,11396,11399,11402,11405],{"className":11363},[168],[50,11365],{"className":11366,"style":3488},[172],[50,11368,11370,11373],{"className":11369},[182],[50,11371,1077],{"className":11372},[182],[50,11374,11376],{"className":11375},[190],[50,11377,11379],{"className":11378},[194],[50,11380,11382],{"className":11381},[199],[50,11383,11385],{"className":11384,"style":3587},[203],[50,11386,11387,11390],{"style":3590},[50,11388],{"className":11389,"style":212},[211],[50,11391,11393],{"className":11392},[216,217,218,219],[50,11394,111],{"className":11395},[182,219],[50,11397,100],{"className":11398},[291],[50,11400],{"className":11401,"style":699},[244],[50,11403,1069],{"className":11404},[703],[50,11406],{"className":11407,"style":699},[244],[50,11409,11411,11414],{"className":11410},[168],[50,11412],{"className":11413,"style":8734},[172],[50,11415,1077],{"className":11416},[182],[11,11418,11419,11420,11423,11424,11452],{},"For this case, an MSE of 0 indicates that the model predicts the real values perfectly for ",[747,11421,11422],{},"this training dataset",". Of course, in practice, real data will contain noise and will be even more variable, so the epsilon ",[50,11425,11427,11440],{"className":11426},[53],[50,11428,11430],{"className":11429},[57],[59,11431,11432],{"xmlns":61},[63,11433,11434,11438],{},[66,11435,11436],{},[80,11437,1133],{},[157,11439,2114],{"encoding":159},[50,11441,11443],{"className":11442,"ariaHidden":89},[164],[50,11444,11446,11449],{"className":11445},[168],[50,11447],{"className":11448,"style":1528},[172],[50,11450,1133],{"className":11451},[182,186]," will not be zero.",[11,11454,11455,11456],{},"To explore more with regression, you can use this Google Colab that contains a complete example of linear regression with Python: ",[18,11457,11463],{"href":11458,"target":11459,"rel":11460},"https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1yi8-fVw2Ak7pqYOzZsiT7NO_zccZrQir?usp=sharing","_blank",[11461,11462],"noopener","noreferrer","linear_regression",[28,11465],{},[39,11467,749],{"id":11468},"classification",[11,11470,11471],{},"A classification problem aims to predict a categorical output variable based on a set of input variables. For example, predicting whether an email is spam or not spam based on its content, or determining whether an image contains a cat or a dog.",[11,11473,11474],{},"There are three main types of classification:",[741,11476,11477,11483,11489],{},[744,11478,11479,11482],{},[747,11480,11481],{},"Binary Classification",": When there are two possible classes. For example, classifying whether a patient has a disease (yes\u002Fno).",[744,11484,11485,11488],{},[747,11486,11487],{},"Multi-class Classification",": When there are more than two possible classes. For example, classifying the type of flower (could be setosa, versicolor or virginica) based on its characteristics.",[744,11490,11491,11494],{},[747,11492,11493],{},"Multi-label Classification",": When each example can belong to multiple classes, such as classifying the labels of a news article (politics, economy, sports) where an article can belong to several categories.",[4606,11496,11497],{},[11,11498,11499],{},"The difference between multi-class and multi-label classification is that in the former each example can only belong to one class, while in the latter an example can belong to multiple classes simultaneously.",[11,11501,11502],{},"Let's look a bit more at binary classification. In this case, the objective is to find a function that maps the inputs to one of the two possible classes.",[11,11504,11505,11506,11509,11510,11624],{},"The question that the binary classification model attempts to answer is: ",[747,11507,11508],{},"What is the probability that an example belongs to class 1 given a set of features?"," This can be expressed mathematically as:\n",[50,11511,11513,11553],{"className":11512},[53],[50,11514,11516],{"className":11515},[57],[59,11517,11518],{"xmlns":61},[63,11519,11520,11550],{},[66,11521,11522,11525,11527,11529,11531,11533,11536,11538,11540,11542,11544,11546,11548],{},[80,11523,11524],{},"P",[69,11526,75],{"stretchy":71},[80,11528,95],{},[69,11530,1069],{},[84,11532,86],{},[80,11534,11535],{"mathvariant":126},"∣",[80,11537,82],{},[69,11539,100],{"stretchy":71},[69,11541,1069],{},[80,11543,666],{},[69,11545,75],{"stretchy":71},[80,11547,82],{},[69,11549,100],{"stretchy":71},[157,11551,11552],{"encoding":159},"P(y=1|x) = f(x)",[50,11554,11556,11581,11606],{"className":11555,"ariaHidden":89},[164],[50,11557,11559,11562,11566,11569,11572,11575,11578],{"className":11558},[168],[50,11560],{"className":11561,"style":173},[172],[50,11563,11524],{"className":11564,"style":11565},[182,186],"margin-right:0.1389em;",[50,11567,75],{"className":11568},[177],[50,11570,95],{"className":11571,"style":252},[182,186],[50,11573],{"className":11574,"style":699},[244],[50,11576,1069],{"className":11577},[703],[50,11579],{"className":11580,"style":699},[244],[50,11582,11584,11587,11591,11594,11597,11600,11603],{"className":11583},[168],[50,11585],{"className":11586,"style":173},[172],[50,11588,11590],{"className":11589},[182],"1∣",[50,11592,82],{"className":11593},[182,186],[50,11595,100],{"className":11596},[291],[50,11598],{"className":11599,"style":699},[244],[50,11601,1069],{"className":11602},[703],[50,11604],{"className":11605,"style":699},[244],[50,11607,11609,11612,11615,11618,11621],{"className":11608},[168],[50,11610],{"className":11611,"style":173},[172],[50,11613,666],{"className":11614,"style":695},[182,186],[50,11616,75],{"className":11617},[177],[50,11619,82],{"className":11620},[182,186],[50,11622,100],{"className":11623},[291],"\nWhere:",[741,11626,11627,11703],{},[744,11628,11629,11702],{},[50,11630,11632,11660],{"className":11631},[53],[50,11633,11635],{"className":11634},[57],[59,11636,11637],{"xmlns":61},[63,11638,11639,11657],{},[66,11640,11641,11643,11645,11647,11649,11651,11653,11655],{},[80,11642,11524],{},[69,11644,75],{"stretchy":71},[80,11646,95],{},[69,11648,1069],{},[84,11650,86],{},[80,11652,11535],{"mathvariant":126},[80,11654,82],{},[69,11656,100],{"stretchy":71},[157,11658,11659],{"encoding":159},"P(y=1|x)",[50,11661,11663,11687],{"className":11662,"ariaHidden":89},[164],[50,11664,11666,11669,11672,11675,11678,11681,11684],{"className":11665},[168],[50,11667],{"className":11668,"style":173},[172],[50,11670,11524],{"className":11671,"style":11565},[182,186],[50,11673,75],{"className":11674},[177],[50,11676,95],{"className":11677,"style":252},[182,186],[50,11679],{"className":11680,"style":699},[244],[50,11682,1069],{"className":11683},[703],[50,11685],{"className":11686,"style":699},[244],[50,11688,11690,11693,11696,11699],{"className":11689},[168],[50,11691],{"className":11692,"style":173},[172],[50,11694,11590],{"className":11695},[182],[50,11697,82],{"className":11698},[182,186],[50,11700,100],{"className":11701},[291]," is the probability that the class is 1 given the feature vector x.",[744,11704,11705,11749],{},[50,11706,11708,11728],{"className":11707},[53],[50,11709,11711],{"className":11710},[57],[59,11712,11713],{"xmlns":61},[63,11714,11715,11725],{},[66,11716,11717,11719,11721,11723],{},[80,11718,666],{},[69,11720,75],{"stretchy":71},[80,11722,82],{},[69,11724,100],{"stretchy":71},[157,11726,11727],{"encoding":159},"f(x)",[50,11729,11731],{"className":11730,"ariaHidden":89},[164],[50,11732,11734,11737,11740,11743,11746],{"className":11733},[168],[50,11735],{"className":11736,"style":173},[172],[50,11738,666],{"className":11739,"style":695},[182,186],[50,11741,75],{"className":11742},[177],[50,11744,82],{"className":11745},[182,186],[50,11747,100],{"className":11748},[291]," is the function that maps the features to the probability.",[11,11751,11752,11753,11756],{},"The most common model for binary classification is the ",[747,11754,11755],{},"logistic regression",", which has the following process:",[996,11758,11759],{},[744,11760,11761,11762,11765],{},"We calculate a ",[747,11763,11764],{},"linear combination"," of the features:",[50,11767,11769],{"className":11768},[650],[50,11770,11772,11845],{"className":11771},[53],[50,11773,11775],{"className":11774},[57],[59,11776,11777],{"xmlns":61,"display":659},[63,11778,11779,11842],{},[66,11780,11781,11784,11786,11792,11794,11800,11806,11808,11814,11820,11822,11824,11826,11828,11830,11836],{},[80,11782,11783],{},"z",[69,11785,1069],{},[77,11787,11788,11790],{},[80,11789,1074],{},[84,11791,1077],{},[69,11793,1080],{},[77,11795,11796,11798],{},[80,11797,1074],{},[84,11799,86],{},[77,11801,11802,11804],{},[80,11803,82],{},[84,11805,86],{},[69,11807,1080],{},[77,11809,11810,11812],{},[80,11811,1074],{},[84,11813,111],{},[77,11815,11816,11818],{},[80,11817,82],{},[84,11819,111],{},[69,11821,1080],{},[80,11823,127],{"mathvariant":126},[80,11825,127],{"mathvariant":126},[80,11827,127],{"mathvariant":126},[69,11829,1080],{},[77,11831,11832,11834],{},[80,11833,1074],{},[80,11835,11],{},[77,11837,11838,11840],{},[80,11839,82],{},[80,11841,11],{},[157,11843,11844],{"encoding":159},"z = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + ... + \\beta_p x_p",[50,11846,11848,11867,11922,12017,12112,12130],{"className":11847,"ariaHidden":89},[164],[50,11849,11851,11854,11858,11861,11864],{"className":11850},[168],[50,11852],{"className":11853,"style":1528},[172],[50,11855,11783],{"className":11856,"style":11857},[182,186],"margin-right:0.044em;",[50,11859],{"className":11860,"style":699},[244],[50,11862,1069],{"className":11863},[703],[50,11865],{"className":11866,"style":699},[244],[50,11868,11870,11873,11913,11916,11919],{"className":11869},[168],[50,11871],{"className":11872,"style":691},[172],[50,11874,11876,11879],{"className":11875},[182],[50,11877,1074],{"className":11878,"style":1170},[182,186],[50,11880,11882],{"className":11881},[190],[50,11883,11885,11905],{"className":11884},[194,195],[50,11886,11888,11902],{"className":11887},[199],[50,11889,11891],{"className":11890,"style":204},[203],[50,11892,11893,11896],{"style":1185},[50,11894],{"className":11895,"style":212},[211],[50,11897,11899],{"className":11898},[216,217,218,219],[50,11900,1077],{"className":11901},[182,219],[50,11903,227],{"className":11904},[226],[50,11906,11908],{"className":11907},[199],[50,11909,11911],{"className":11910,"style":234},[203],[50,11912],{},[50,11914],{"className":11915,"style":736},[244],[50,11917,1080],{"className":11918},[1212],[50,11920],{"className":11921,"style":736},[244],[50,11923,11925,11928,11968,12008,12011,12014],{"className":11924},[168],[50,11926],{"className":11927,"style":691},[172],[50,11929,11931,11934],{"className":11930},[182],[50,11932,1074],{"className":11933,"style":1170},[182,186],[50,11935,11937],{"className":11936},[190],[50,11938,11940,11960],{"className":11939},[194,195],[50,11941,11943,11957],{"className":11942},[199],[50,11944,11946],{"className":11945,"style":204},[203],[50,11947,11948,11951],{"style":1185},[50,11949],{"className":11950,"style":212},[211],[50,11952,11954],{"className":11953},[216,217,218,219],[50,11955,86],{"className":11956},[182,219],[50,11958,227],{"className":11959},[226],[50,11961,11963],{"className":11962},[199],[50,11964,11966],{"className":11965,"style":234},[203],[50,11967],{},[50,11969,11971,11974],{"className":11970},[182],[50,11972,82],{"className":11973},[182,186],[50,11975,11977],{"className":11976},[190],[50,11978,11980,12000],{"className":11979},[194,195],[50,11981,11983,11997],{"className":11982},[199],[50,11984,11986],{"className":11985,"style":204},[203],[50,11987,11988,11991],{"style":207},[50,11989],{"className":11990,"style":212},[211],[50,11992,11994],{"className":11993},[216,217,218,219],[50,11995,86],{"className":11996},[182,219],[50,11998,227],{"className":11999},[226],[50,12001,12003],{"className":12002},[199],[50,12004,12006],{"className":12005,"style":234},[203],[50,12007],{},[50,12009],{"className":12010,"style":736},[244],[50,12012,1080],{"className":12013},[1212],[50,12015],{"className":12016,"style":736},[244],[50,12018,12020,12023,12063,12103,12106,12109],{"className":12019},[168],[50,12021],{"className":12022,"style":691},[172],[50,12024,12026,12029],{"className":12025},[182],[50,12027,1074],{"className":12028,"style":1170},[182,186],[50,12030,12032],{"className":12031},[190],[50,12033,12035,12055],{"className":12034},[194,195],[50,12036,12038,12052],{"className":12037},[199],[50,12039,12041],{"className":12040,"style":204},[203],[50,12042,12043,12046],{"style":1185},[50,12044],{"className":12045,"style":212},[211],[50,12047,12049],{"className":12048},[216,217,218,219],[50,12050,111],{"className":12051},[182,219],[50,12053,227],{"className":12054},[226],[50,12056,12058],{"className":12057},[199],[50,12059,12061],{"className":12060,"style":234},[203],[50,12062],{},[50,12064,12066,12069],{"className":12065},[182],[50,12067,82],{"className":12068},[182,186],[50,12070,12072],{"className":12071},[190],[50,12073,12075,12095],{"className":12074},[194,195],[50,12076,12078,12092],{"className":12077},[199],[50,12079,12081],{"className":12080,"style":204},[203],[50,12082,12083,12086],{"style":207},[50,12084],{"className":12085,"style":212},[211],[50,12087,12089],{"className":12088},[216,217,218,219],[50,12090,111],{"className":12091},[182,219],[50,12093,227],{"className":12094},[226],[50,12096,12098],{"className":12097},[199],[50,12099,12101],{"className":12100,"style":234},[203],[50,12102],{},[50,12104],{"className":12105,"style":736},[244],[50,12107,1080],{"className":12108},[1212],[50,12110],{"className":12111,"style":736},[244],[50,12113,12115,12118,12121,12124,12127],{"className":12114},[168],[50,12116],{"className":12117,"style":1412},[172],[50,12119,399],{"className":12120},[182],[50,12122],{"className":12123,"style":736},[244],[50,12125,1080],{"className":12126},[1212],[50,12128],{"className":12129,"style":736},[244],[50,12131,12133,12136,12176],{"className":12132},[168],[50,12134],{"className":12135,"style":1431},[172],[50,12137,12139,12142],{"className":12138},[182],[50,12140,1074],{"className":12141,"style":1170},[182,186],[50,12143,12145],{"className":12144},[190],[50,12146,12148,121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probability:",[50,12425,12427],{"className":12426},[650],[50,12428,12430,12548],{"className":12429},[53],[50,12431,12433],{"className":12432},[57],[59,12434,12435],{"xmlns":61,"display":659},[63,12436,12437,12545],{},[66,12438,12439,12441,12443,12445,12447,12449,12451,12453,12455,12457,12459,12461,12463,12465,12467],{},[80,12440,11524],{},[69,12442,75],{"stretchy":71},[80,12444,95],{},[69,12446,1069],{},[84,12448,86],{},[80,12450,11535],{"mathvariant":126},[80,12452,82],{},[69,12454,100],{"stretchy":71},[69,12456,1069],{},[80,12458,12242],{},[69,12460,75],{"stretchy":71},[80,12462,11783],{},[69,12464,100],{"stretchy":71},[69,12466,1069],{},[3192,12468,12469,12471],{},[84,12470,86],{},[66,12472,12473,12475,12477],{},[84,12474,86],{},[69,12476,1080],{},[3235,12478,12479,12481],{},[80,12480,10469],{},[66,12482,12483,12485,12487,12493,12495,12501,12507,12509,12515,12521,12523,12525,12527,12529,12531,12537,12543],{},[69,12484,2587],{},[69,12486,75],{"stretchy":71},[77,12488,12489,12491],{},[80,12490,1074],{},[84,12492,1077],{},[69,12494,1080],{},[77,12496,12497,12499],{},[80,12498,1074],{},[84,12500,86],{},[77,12502,12503,12505],{},[80,12504,82],{},[84,12506,86],{},[69,12508,1080],{},[77,12510,12511,12513],{},[80,12512,1074],{},[84,12514,111],{},[77,12516,12517,12519],{},[80,12518,82],{},[84,12520,111],{},[69,12522,1080],{},[80,12524,127],{"mathvariant":126},[80,12526,127],{"mathvariant":126},[80,12528,127],{"mathvariant":126},[69,12530,1080],{},[77,12532,12533,12535],{},[80,12534,1074],{},[80,12536,11],{},[77,12538,12539,12541],{},[80,12540,82],{},[80,12542,11],{},[69,12544,100],{"stretchy":71},[157,12546,12547],{"encoding":159},"P(y=1|x) 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is the 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are the features or independent variables.",[744,13520,13521,11702],{},[50,13522,13524,13551],{"className":13523},[53],[50,13525,13527],{"className":13526},[57],[59,13528,13529],{"xmlns":61},[63,13530,13531,13549],{},[66,13532,13533,13535,13537,13539,13541,13543,13545,13547],{},[80,13534,11524],{},[69,13536,75],{"stretchy":71},[80,13538,95],{},[69,13540,1069],{},[84,13542,86],{},[80,13544,11535],{"mathvariant":126},[80,13546,82],{},[69,13548,100],{"stretchy":71},[157,13550,11659],{"encoding":159},[50,13552,13554,13578],{"className":13553,"ariaHidden":89},[164],[50,13555,13557,13560,13563,13566,13569,13572,13575],{"className":13556},[168],[50,13558],{"className":13559,"style":173},[172],[50,13561,11524],{"className":13562,"style":11565},[182,186],[50,13564,75],{"className":13565},[177],[50,13567,95],{"className":13568,"style":252},[182,186],[50,13570],{"className":13571,"style":699},[244],[50,13573,1069],{"className":13574},[703],[50,13576],{"className":13577,"style":699},[244],[50,13579,13581,13584,13587,13590],{"className":13580},[168],[50,13582],{"className":13583,"style":173},[172],[50,13585,11590],{"className":13586},[182],[50,13588,82],{"className":13589},[182,186],[50,13591,100],{"className":13592},[291],[11,13594,13595],{},"Each feature provides evidence for or against a particular class. For example, if the word \"free\" appears in an email, it might increase the probability that it is spam. On the other hand, if the word \"meeting\" appears, it might decrease the probability of it being spam.",[4606,13597,13598],{},[11,13599,13600],{},"This gives us not only a classification but also a measure of confidence in that classification through the probability calculated by the sigmoid function.",[11,13602,13603],{},"The sigmoid function transforms any real value into a value between 0 and 1. The formula as shown above is:",[50,13605,13607],{"className":13606},[650],[50,13608,13610,13651],{"className":13609},[53],[50,13611,13613],{"className":13612},[57],[59,13614,13615],{"xmlns":61,"display":659},[63,13616,13617,13649],{},[66,13618,13619,13621,13623,13625,13627,13629],{},[80,13620,12242],{},[69,13622,75],{"stretchy":71},[80,13624,11783],{},[69,13626,100],{"stretchy":71},[69,13628,1069],{},[3192,13630,13631,13633],{},[84,13632,86],{},[66,13634,13635,13637,13639],{},[84,13636,86],{},[69,13638,1080],{},[3235,13640,13641,13643],{},[80,13642,10469],{},[66,13644,13645,13647],{},[69,13646,2587],{},[80,13648,11783],{},[157,13650,12273],{"encoding":159},[50,13652,13654,13681],{"className":13653,"ariaHidden":89},[164],[50,13655,13657,13660,13663,13666,13669,13672,13675,13678],{"className":13656},[168],[50,13658],{"className":13659,"style":173},[172],[50,13661,12242],{"className":13662,"style":252},[182,186],[50,13664,75],{"className":13665},[177],[50,13667,11783],{"className":13668,"style":11857},[182,186],[50,13670,100],{"className":13671},[291],[50,13673],{"className":13674,"style":699},[244],[50,13676,1069],{"className":13677},[703],[50,13679],{"className":13680,"style":699},[244],[50,13682,13684,13687],{"className":13683},[168],[50,13685],{"className":13686,"style":12310},[172],[50,13688,13690,13693,13790],{"className":13689},[182],[50,13691],{"className":13692},[177,3286],[50,13694,13696],{"className":13695},[3192],[50,13697,13699,13782],{"className":13698},[194,195],[50,13700,13702,13779],{"className":13701},[199],[50,13703,13705,13760,13768],{"className":13704,"style":3299},[203],[50,13706,13707,13710],{"style":3302},[50,13708],{"className":13709,"style":2754},[211],[50,13711,13713,13716,13719,13722,13725],{"className":13712},[182],[50,13714,86],{"className":13715},[182],[50,13717],{"className":13718,"style":736},[244],[50,13720,1080],{"className":13721},[1212],[50,13723],{"className":13724,"style":736},[244],[50,13726,13728,13731],{"className":13727},[182],[50,13729,10469],{"className":13730},[182,186],[50,13732,13734],{"className":13733},[190],[50,13735,13737],{"className":13736},[194],[50,13738,13740],{"className":13739},[199],[50,13741,13743],{"className":13742,"style":12367},[203],[50,13744,13745,13748],{"style":5138},[50,13746],{"className":13747,"style":212},[211],[50,13749,13751],{"className":13750},[216,217,218,219],[50,13752,13754,13757],{"className":13753},[182,219],[50,13755,2587],{"className":13756},[182,219],[50,13758,11783],{"className":13759,"style":11857},[182,186,219],[50,13761,13762,13765],{"style":3314},[50,13763],{"className":13764,"style":2754},[211],[50,13766],{"className":13767,"style":3322},[3321],[50,13769,13770,13773],{"style":3325},[50,13771],{"className":13772,"style":2754},[211],[50,13774,13776],{"className":13775},[182],[50,13777,86],{"className":13778},[182],[50,13780,227],{"className":13781},[226],[50,13783,13785],{"className":13784},[199],[50,13786,13788],{"className":13787,"style":12413},[203],[50,13789],{},[50,13791],{"className":13792},[291,3286],[11,13794,1534],{},[741,13796,13797,14272,14303],{},[744,13798,13799,13827,13828,127],{},[50,13800,13802,13815],{"className":13801},[53],[50,13803,13805],{"className":13804},[57],[59,13806,13807],{"xmlns":61},[63,13808,13809,13813],{},[66,13810,13811],{},[80,13812,11783],{},[157,13814,11783],{"encoding":159},[50,13816,13818],{"className":13817,"ariaHidden":89},[164],[50,13819,13821,13824],{"className":13820},[168],[50,13822],{"className":13823,"style":1528},[172],[50,13825,11783],{"className":13826,"style":11857},[182,186]," is the linear combination of the features, that is, ",[50,13829,13831,13902],{"className":13830},[53],[50,13832,13834],{"className":13833},[57],[59,13835,13836],{"xmlns":61},[63,13837,13838,13900],{},[66,13839,13840,13842,13844,13850,13852,13858,13864,13866,13872,13878,13880,13882,13884,13886,13888,13894],{},[80,13841,11783],{},[69,13843,1069],{},[77,13845,13846,13848],{},[80,13847,1074],{},[84,13849,1077],{},[69,13851,1080],{},[77,13853,13854,13856],{},[80,13855,1074],{},[84,13857,86],{},[77,13859,13860,13862],{},[80,13861,82],{},[84,13863,86],{},[69,13865,1080],{},[77,13867,13868,13870],{},[80,13869,1074],{},[84,13871,111],{},[77,13873,13874,13876],{},[80,13875,82],{},[84,13877,111],{},[69,13879,1080],{},[80,13881,127],{"mathvariant":126},[80,13883,127],{"mathvariant":126},[80,13885,127],{"mathvariant":126},[69,13887,1080],{},[77,13889,13890,13892],{},[80,13891,1074],{},[80,13893,11],{},[77,13895,13896,13898],{},[80,13897,82],{},[80,13899,11],{},[157,13901,11844],{"encoding":159},[50,13903,13905,13923,13978,14073,14168,14186],{"className":13904,"ariaHidden":89},[164],[50,13906,13908,13911,13914,13917,13920],{"className":13907},[168],[50,13909],{"className":13910,"style":1528},[172],[50,13912,11783],{"className":13913,"style":11857},[182,186],[50,13915],{"className":13916,"style":699},[244],[50,13918,1069],{"className":13919},[703],[50,13921],{"className":13922,"style":699},[244],[50,13924,13926,13929,13969,13972,13975],{"className":13925},[168],[50,13927],{"className":13928,"style":691},[172],[50,13930,13932,13935],{"className":13931},[182],[50,13933,1074],{"className":13934,"style":1170},[182,186],[50,13936,13938],{"className":13937},[190],[50,13939,13941,13961],{"className":13940},[194,195],[50,13942,13944,13958],{"className":13943},[199],[50,13945,13947],{"className":13946,"style":204},[203],[50,13948,13949,13952],{"style":1185},[50,13950],{"className":13951,"style":212},[211],[50,13953,13955],{"className":13954},[216,217,218,219],[50,13956,1077],{"className":13957},[182,219],[50,13959,227],{"className":13960},[226],[50,13962,13964],{"className":13963},[199],[50,13965,13967],{"className":13966,"style":234},[203],[50,13968],{},[50,13970],{"className":13971,"style":736},[244],[50,13973,1080],{"className":13974},[1212],[50,13976],{"className":13977,"style":736},[244],[50,13979,13981,13984,14024,14064,14067,14070],{"className":13980},[168],[50,13982],{"className":13983,"style":691},[172],[50,13985,13987,13990],{"className":13986},[182],[50,13988,1074],{"className":13989,"style":1170},[182,186],[50,13991,13993],{"className":13992},[190],[50,13994,13996,14016],{"className":13995},[194,195],[50,13997,13999,14013],{"className":13998},[199],[50,14000,14002],{"className":14001,"style":204},[203],[50,14003,14004,14007],{"style":1185},[50,14005],{"className":14006,"style":212},[211],[50,14008,14010],{"className":14009},[216,217,218,219],[50,14011,86],{"className":14012},[182,219],[50,14014,227],{"className":14015},[226],[50,14017,14019],{"className":14018},[199],[50,14020,14022],{"className":14021,"style":234},[203],[50,14023],{},[50,14025,14027,14030],{"className":14026},[182],[50,14028,82],{"className":14029},[182,186],[50,14031,14033],{"className":14032},[190],[50,14034,14036,14056],{"className":14035},[194,195],[50,14037,14039,14053],{"className":14038},[199],[50,14040,14042],{"className":14041,"style":204},[203],[50,14043,14044,14047],{"style":207},[50,14045],{"className":14046,"style":212},[211],[50,14048,14050],{"className":14049},[216,217,218,219],[50,14051,86],{"className":14052},[182,219],[50,14054,227],{"className":14055},[226],[50,14057,14059],{"className":14058},[199],[50,14060,14062],{"className":14061,"style":234},[203],[50,14063],{},[50,14065],{"className":14066,"style":736},[244],[50,14068,1080],{"className":14069},[1212],[50,14071],{"className":14072,"style":736},[244],[50,14074,14076,14079,14119,14159,14162,14165],{"className":14075},[168],[50,14077],{"className":14078,"style":691},[172],[50,14080,14082,14085],{"className":14081},[182],[50,14083,1074],{"className":14084,"style":1170},[182,186],[50,14086,14088],{"className":14087},[190],[50,14089,14091,14111],{"className":14090},[194,195],[50,14092,14094,14108],{"className":14093},[199],[50,14095,14097],{"className":14096,"style":204},[203],[50,14098,14099,14102],{"style":1185},[50,14100],{"className":14101,"style":212},[211],[50,14103,14105],{"className":14104},[216,217,218,219],[50,14106,111],{"className":14107},[182,219],[50,14109,227],{"className":14110},[226],[50,14112,14114],{"className":14113},[199],[50,14115,14117],{"className":14116,"style":234},[203],[50,14118],{},[50,14120,14122,14125],{"className":14121},[182],[50,14123,82],{"className":14124},[182,186],[50,14126,14128],{"className":14127},[190],[50,14129,14131,14151],{"className":14130},[194,195],[50,14132,14134,14148],{"className":14133},[199],[50,14135,14137],{"className":14136,"style":204},[203],[50,14138,14139,14142],{"style":207},[50,14140],{"className":14141,"style":212},[211],[50,14143,14145],{"className":14144},[216,217,218,219],[50,14146,111],{"className":14147},[182,219],[50,14149,227],{"className":14150},[226],[50,14152,14154],{"className":14153},[199],[50,14155,14157],{"className":14156,"style":234},[203],[50,14158],{},[50,14160],{"className":14161,"style":736},[244],[50,14163,1080],{"className":14164},[1212],[50,14166],{"className":14167,"style":736},[244],[50,14169,14171,14174,14177,14180,14183],{"className":14170},[168],[50,14172],{"className":14173,"style":1412},[172],[50,14175,399],{"className":14176},[182],[50,14178],{"className":14179,"style":736},[244],[50,14181,1080],{"className":14182},[1212],[50,14184],{"className":14185,"style":736},[244],[50,14187,14189,14192,14232],{"className":14188},[168],[50,14190],{"className":14191,"style":1431},[172],[50,14193,14195,14198],{"className":14194},[182],[50,14196,1074],{"className":14197,"style":1170},[182,186],[50,14199,14201],{"className":14200},[190],[50,14202,14204,14224],{"className":14203},[194,195],[50,14205,14207,14221],{"className":14206},[199],[50,14208,14210],{"className":14209,"style":427},[203],[50,14211,14212,14215],{"style":1185},[50,14213],{"className":14214,"style":212},[211],[50,14216,14218],{"className":14217},[216,217,218,219],[50,14219,11],{"className":14220},[182,186,219],[50,14222,227],{"className":14223},[226],[50,14225,14227],{"className":14226},[199],[50,14228,14230],{"className":14229,"style":1470},[203],[50,14231],{},[50,14233,14235,14238],{"className":14234},[182],[50,14236,82],{"className":14237},[182,186],[50,14239,14241],{"className":14240},[190],[50,14242,14244,14264],{"className":14243},[194,195],[50,14245,14247,14261],{"className":14246},[199],[50,14248,14250],{"className":14249,"style":427},[203],[50,14251,14252,14255],{"style":207},[50,14253],{"className":14254,"style":212},[211],[50,14256,14258],{"className":14257},[216,217,218,219],[50,14259,11],{"className":14260},[182,186,219],[50,14262,227],{"className":14263},[226],[50,14265,14267],{"className":14266},[199],[50,14268,14270],{"className":14269,"style":1470},[203],[50,14271],{},[744,14273,14274,14302],{},[50,14275,14277,14290],{"className":14276},[53],[50,14278,14280],{"className":14279},[57],[59,14281,14282],{"xmlns":61},[63,14283,14284,14288],{},[66,14285,14286],{},[80,14287,10469],{},[157,14289,10469],{"encoding":159},[50,14291,14293],{"className":14292,"ariaHidden":89},[164],[50,14294,14296,14299],{"className":14295},[168],[50,14297],{"className":14298,"style":1528},[172],[50,14300,10469],{"className":14301},[182,186]," is Euler's number, approximately equal to 2.71828.",[744,14304,14305,14349],{},[50,14306,14308,14328],{"className":14307},[53],[50,14309,14311],{"className":14310},[57],[59,14312,14313],{"xmlns":61},[63,14314,14315,14325],{},[66,14316,14317,14319,14321,14323],{},[80,14318,12242],{},[69,14320,75],{"stretchy":71},[80,14322,11783],{},[69,14324,100],{"stretchy":71},[157,14326,14327],{"encoding":159},"\\sigma(z)",[50,14329,14331],{"className":14330,"ariaHidden":89},[164],[50,14332,14334,14337,14340,14343,14346],{"className":14333},[168],[50,14335],{"className":14336,"style":173},[172],[50,14338,12242],{"className":14339,"style":252},[182,186],[50,14341,75],{"className":14342},[177],[50,14344,11783],{"className":14345,"style":11857},[182,186],[50,14347,100],{"className":14348},[291]," is the output of the sigmoid function, which represents the probability of the class being 1 given the value of z (range between 0 and 1).",[11,14351,14352,14353,14396,14397,14401],{},"Visually it looks something like this (with z on the X-axis and ",[50,14354,14356,14375],{"className":14355},[53],[50,14357,14359],{"className":14358},[57],[59,14360,14361],{"xmlns":61},[63,14362,14363,14373],{},[66,14364,14365,14367,14369,14371],{},[80,14366,12242],{},[69,14368,75],{"stretchy":71},[80,14370,11783],{},[69,14372,100],{"stretchy":71},[157,14374,14327],{"encoding":159},[50,14376,14378],{"className":14377,"ariaHidden":89},[164],[50,14379,14381,14384,14387,14390,14393],{"className":14380},[168],[50,14382],{"className":14383,"style":173},[172],[50,14385,12242],{"className":14386,"style":252},[182,186],[50,14388,75],{"className":14389},[177],[50,14391,11783],{"className":14392,"style":11857},[182,186],[50,14394,100],{"className":14395},[291]," on the Y-axis):\n",[2432,14398],{"alt":14399,"src":14400},"Sigmoid Function Graph","\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations\u002Fshared\u002Fsigmoid-function.webp",[2437,14402,14399],{},[11,14404,14405],{},"From this we can draw some important conclusions:",[741,14407,14408,14455,14502],{},[744,14409,14410,14411,14454],{},"When z is very negative, ",[50,14412,14414,14433],{"className":14413},[53],[50,14415,14417],{"className":14416},[57],[59,14418,14419],{"xmlns":61},[63,14420,14421,14431],{},[66,14422,14423,14425,14427,14429],{},[80,14424,12242],{},[69,14426,75],{"stretchy":71},[80,14428,11783],{},[69,14430,100],{"stretchy":71},[157,14432,14327],{"encoding":159},[50,14434,14436],{"className":14435,"ariaHidden":89},[164],[50,14437,14439,14442,14445,14448,14451],{"className":14438},[168],[50,14440],{"className":14441,"style":173},[172],[50,14443,12242],{"className":14444,"style":252},[182,186],[50,14446,75],{"className":14447},[177],[50,14449,11783],{"className":14450,"style":11857},[182,186],[50,14452,100],{"className":14453},[291]," approaches 0, indicating a low probability that the class is 1.",[744,14456,14457,14458,14501],{},"When z is very positive, ",[50,14459,14461,14480],{"className":14460},[53],[50,14462,14464],{"className":14463},[57],[59,14465,14466],{"xmlns":61},[63,14467,14468,14478],{},[66,14469,14470,14472,14474,14476],{},[80,14471,12242],{},[69,14473,75],{"stretchy":71},[80,14475,11783],{},[69,14477,100],{"stretchy":71},[157,14479,14327],{"encoding":159},[50,14481,14483],{"className":14482,"ariaHidden":89},[164],[50,14484,14486,14489,14492,14495,14498],{"className":14485},[168],[50,14487],{"className":14488,"style":173},[172],[50,14490,12242],{"className":14491,"style":252},[182,186],[50,14493,75],{"className":14494},[177],[50,14496,11783],{"className":14497,"style":11857},[182,186],[50,14499,100],{"className":14500},[291]," approaches 1, indicating a high probability that the class is 1.",[744,14503,14504,14505,14548],{},"When z is 0, ",[50,14506,14508,14527],{"className":14507},[53],[50,14509,14511],{"className":14510},[57],[59,14512,14513],{"xmlns":61},[63,14514,14515,14525],{},[66,14516,14517,14519,14521,14523],{},[80,14518,12242],{},[69,14520,75],{"stretchy":71},[80,14522,11783],{},[69,14524,100],{"stretchy":71},[157,14526,14327],{"encoding":159},[50,14528,14530],{"className":14529,"ariaHidden":89},[164],[50,14531,14533,14536,14539,14542,14545],{"className":14532},[168],[50,14534],{"className":14535,"style":173},[172],[50,14537,12242],{"className":14538,"style":252},[182,186],[50,14540,75],{"className":14541},[177],[50,14543,11783],{"className":14544,"style":11857},[182,186],[50,14546,100],{"className":14547},[291]," is 0.5, indicating an equal probability that the class is 0 or 1.",[11,14550,14551,14552,14555],{},"The value of 0.5 ",[747,14553,14554],{},"is commonly used as a threshold",", so that:",[741,14557,14558,14630],{},[744,14559,14560,14561,14629],{},"If ",[50,14562,14564,14590],{"className":14563},[53],[50,14565,14567],{"className":14566},[57],[59,14568,14569],{"xmlns":61},[63,14570,14571,14587],{},[66,14572,14573,14575,14577,14579,14581,14584],{},[80,14574,12242],{},[69,14576,75],{"stretchy":71},[80,14578,11783],{},[69,14580,100],{"stretchy":71},[69,14582,14583],{},"≥",[84,14585,14586],{},"0.5",[157,14588,14589],{"encoding":159},"\\sigma(z) \\geq 0.5",[50,14591,14593,14620],{"className":14592,"ariaHidden":89},[164],[50,14594,14596,14599,14602,14605,14608,14611,14614,14617],{"className":14595},[168],[50,14597],{"className":14598,"style":173},[172],[50,14600,12242],{"className":14601,"style":252},[182,186],[50,14603,75],{"className":14604},[177],[50,14606,11783],{"className":14607,"style":11857},[182,186],[50,14609,100],{"className":14610},[291],[50,14612],{"className":14613,"style":699},[244],[50,14615,14583],{"className":14616},[703],[50,14618],{"className":14619,"style":699},[244],[50,14621,14623,14626],{"className":14622},[168],[50,14624],{"className":14625,"style":8734},[172],[50,14627,14586],{"className":14628},[182],", it is classified as class 1.",[744,14631,14560,14632,14699],{},[50,14633,14635,14660],{"className":14634},[53],[50,14636,14638],{"className":14637},[57],[59,14639,14640],{"xmlns":61},[63,14641,14642,14657],{},[66,14643,14644,14646,14648,14650,14652,14655],{},[80,14645,12242],{},[69,14647,75],{"stretchy":71},[80,14649,11783],{},[69,14651,100],{"stretchy":71},[69,14653,14654],{},"\u003C",[84,14656,14586],{},[157,14658,14659],{"encoding":159},"\\sigma(z) \u003C 0.5",[50,14661,14663,14690],{"className":14662,"ariaHidden":89},[164],[50,14664,14666,14669,14672,14675,14678,14681,14684,14687],{"className":14665},[168],[50,14667],{"className":14668,"style":173},[172],[50,14670,12242],{"className":14671,"style":252},[182,186],[50,14673,75],{"className":14674},[177],[50,14676,11783],{"className":14677,"style":11857},[182,186],[50,14679,100],{"className":14680},[291],[50,14682],{"className":14683,"style":699},[244],[50,14685,14654],{"className":14686},[703],[50,14688],{"className":14689,"style":699},[244],[50,14691,14693,14696],{"className":14692},[168],[50,14694],{"className":14695,"style":8734},[172],[50,14697,14586],{"className":14698},[182],", it is classified as class 0.",[4606,14701,14702,14705],{},[11,14703,14704],{},"Key properties:",[741,14706,14707,14710,14757],{},[744,14708,14709],{},"Bounded range between 0 and 1",[744,14711,14712,14713,14756],{},"Monotonicity: if z increases, ",[50,14714,14716,14735],{"className":14715},[53],[50,14717,14719],{"className":14718},[57],[59,14720,14721],{"xmlns":61},[63,14722,14723,14733],{},[66,14724,14725,14727,14729,14731],{},[80,14726,12242],{},[69,14728,75],{"stretchy":71},[80,14730,11783],{},[69,14732,100],{"stretchy":71},[157,14734,14327],{"encoding":159},[50,14736,14738],{"className":14737,"ariaHidden":89},[164],[50,14739,14741,14744,14747,14750,14753],{"className":14740},[168],[50,14742],{"className":14743,"style":173},[172],[50,14745,12242],{"className":14746,"style":252},[182,186],[50,14748,75],{"className":14749},[177],[50,14751,11783],{"className":14752,"style":11857},[182,186],[50,14754,100],{"className":14755},[291]," also increases",[744,14758,14759,14760,14826,14827,4718,14879,14826,14945],{},"Asymptotic: ",[50,14761,14763,14787],{"className":14762},[53],[50,14764,14766],{"className":14765},[57],[59,14767,14768],{"xmlns":61},[63,14769,14770,14784],{},[66,14771,14772,14774,14776,14778,14780,14782],{},[80,14773,12242],{},[69,14775,75],{"stretchy":71},[80,14777,11783],{},[69,14779,100],{"stretchy":71},[69,14781,675],{},[84,14783,86],{},[157,14785,14786],{"encoding":159},"\\sigma(z) \\to 1",[50,14788,14790,14817],{"className":14789,"ariaHidden":89},[164],[50,14791,14793,14796,14799,14802,14805,14808,14811,14814],{"className":14792},[168],[50,14794],{"className":14795,"style":173},[172],[50,14797,12242],{"className":14798,"style":252},[182,186],[50,14800,75],{"className":14801},[177],[50,14803,11783],{"className":14804,"style":11857},[182,186],[50,14806,100],{"className":14807},[291],[50,14809],{"className":14810,"style":699},[244],[50,14812,675],{"className":14813},[703],[50,14815],{"className":14816,"style":699},[244],[50,14818,14820,14823],{"className":14819},[168],[50,14821],{"className":14822,"style":8734},[172],[50,14824,86],{"className":14825},[182]," when ",[50,14828,14830,14849],{"className":14829},[53],[50,14831,14833],{"className":14832},[57],[59,14834,14835],{"xmlns":61},[63,14836,14837,14846],{},[66,14838,14839,14841,14843],{},[80,14840,11783],{},[69,14842,675],{},[80,14844,14845],{"mathvariant":126},"∞",[157,14847,14848],{"encoding":159},"z \\to \\infty",[50,14850,14852,14870],{"className":14851,"ariaHidden":89},[164],[50,14853,14855,14858,14861,14864,14867],{"className":14854},[168],[50,14856],{"className":14857,"style":1528},[172],[50,14859,11783],{"className":14860,"style":11857},[182,186],[50,14862],{"className":14863,"style":699},[244],[50,14865,675],{"className":14866},[703],[50,14868],{"className":14869,"style":699},[244],[50,14871,14873,14876],{"className":14872},[168],[50,14874],{"className":14875,"style":1528},[172],[50,14877,14845],{"className":14878},[182],[50,14880,14882,14906],{"className":14881},[53],[50,14883,14885],{"className":14884},[57],[59,14886,14887],{"xmlns":61},[63,14888,14889,14903],{},[66,14890,14891,14893,14895,14897,14899,14901],{},[80,14892,12242],{},[69,14894,75],{"stretchy":71},[80,14896,11783],{},[69,14898,100],{"stretchy":71},[69,14900,675],{},[84,14902,1077],{},[157,14904,14905],{"encoding":159},"\\sigma(z) \\to 0",[50,14907,14909,14936],{"className":14908,"ariaHidden":89},[164],[50,14910,14912,14915,14918,14921,14924,14927,14930,14933],{"className":14911},[168],[50,14913],{"className":14914,"style":173},[172],[50,14916,12242],{"className":14917,"style":252},[182,186],[50,14919,75],{"className":14920},[177],[50,14922,11783],{"className":14923,"style":11857},[182,186],[50,14925,100],{"className":14926},[291],[50,14928],{"className":14929,"style":699},[244],[50,14931,675],{"className":14932},[703],[50,14934],{"className":14935,"style":699},[244],[50,14937,14939,14942],{"className":14938},[168],[50,14940],{"className":14941,"style":8734},[172],[50,14943,1077],{"className":14944},[182],[50,14946,14948,14968],{"className":14947},[53],[50,14949,14951],{"className":14950},[57],[59,14952,14953],{"xmlns":61},[63,14954,14955,14965],{},[66,14956,14957,14959,14961,14963],{},[80,14958,11783],{},[69,14960,675],{},[69,14962,2587],{},[80,14964,14845],{"mathvariant":126},[157,14966,14967],{"encoding":159},"z \\to -\\infty",[50,14969,14971,14989],{"className":14970,"ariaHidden":89},[164],[50,14972,14974,14977,14980,14983,14986],{"className":14973},[168],[50,14975],{"className":14976,"style":1528},[172],[50,14978,11783],{"className":14979,"style":11857},[182,186],[50,14981],{"className":14982,"style":699},[244],[50,14984,675],{"className":14985},[703],[50,14987],{"className":14988,"style":699},[244],[50,14990,14992,14995,14998],{"className":14991},[168],[50,14993],{"className":14994,"style":1412},[172],[50,14996,2587],{"className":14997},[182],[50,14999,14845],{"className":15000},[182],[11,15002,15003,15006,15007,15010,15011,669],{},[747,15004,15005],{},"How do we measure error in classification?"," Here we use metrics like ",[747,15008,15009],{},"cross-entropy"," or ",[747,15012,15013],{},"log loss",[11,15015,15016],{},"Loss for a single sample:",[50,15018,15020],{"className":15019},[650],[50,15021,15023,15101],{"className":15022},[53],[50,15024,15026],{"className":15025},[57],[59,15027,15028],{"xmlns":61,"display":659},[63,15029,15030,15098],{},[66,15031,15032,15035,15037,15039,15042,15044,15047,15050,15053,15055,15061,15063,15065,15067,15069,15071,15073,15075,15077,15079,15081,15083,15085,15087,15093,15095],{},[80,15033,15034],{},"L",[69,15036,1069],{},[69,15038,2587],{},[69,15040,15041],{"stretchy":71},"[",[80,15043,95],{},[69,15045,15046],{},"⋅",[80,15048,15049],{},"log",[69,15051,15052],{},"⁡",[69,15054,75],{"stretchy":71},[2589,15056,15057,15059],{"accent":89},[80,15058,95],{},[69,15060,2599],{},[69,15062,100],{"stretchy":71},[69,15064,1080],{},[69,15066,75],{"stretchy":71},[84,15068,86],{},[69,15070,2587],{},[80,15072,95],{},[69,15074,100],{"stretchy":71},[69,15076,15046],{},[80,15078,15049],{},[69,15080,15052],{},[69,15082,75],{"stretchy":71},[84,15084,86],{},[69,15086,2587],{},[2589,15088,15089,15091],{"accent":89},[80,15090,95],{},[69,15092,2599],{},[69,15094,100],{"stretchy":71},[69,15096,15097],{"stretchy":71},"]",[157,15099,15100],{"encoding":159},"L = -[y \\cdot \\log(\\hat{y}) + (1 - y) \\cdot \\log(1 - \\hat{y})]",[50,15102,15104,15122,15146,15217,15238,15259,15285],{"className":15103,"ariaHidden":89},[164],[50,15105,15107,15110,15113,15116,15119],{"className":15106},[168],[50,15108],{"className":15109,"style":713},[172],[50,15111,15034],{"className":15112},[182,186],[50,15114],{"className":15115,"style":699},[244],[50,15117,1069],{"className":15118},[703],[50,15120],{"className":15121,"style":699},[244],[50,15123,15125,15128,15131,15134,15137,15140,15143],{"className":15124},[168],[50,15126],{"className":15127,"style":173},[172],[50,15129,2587],{"className":15130},[182],[50,15132,15041],{"className":15133},[177],[50,15135,95],{"className":15136,"style":252},[182,186],[50,15138],{"className":15139,"style":736},[244],[50,15141,15046],{"className":15142},[1212],[50,15144],{"className":15145,"style":736},[244],[50,15147,15149,15152,15160,15163,15205,15208,15211,15214],{"className":15148},[168],[50,15150],{"className":15151,"style":173},[172],[50,15153,15155,15156],{"className":15154},[3356],"lo",[50,15157,15159],{"style":15158},"margin-right:0.0139em;","g",[50,15161,75],{"className":15162},[177],[50,15164,15166],{"className":15165},[182,2737],[50,15167,15169,15197],{"className":15168},[194,195],[50,15170,15172,15194],{"className":15171},[199],[50,15173,15175,15183],{"className":15174,"style":3501},[203],[50,15176,15177,15180],{"style":2750},[50,15178],{"className":15179,"style":2754},[211],[50,15181,95],{"className":15182,"style":252},[182,186],[50,15184,15185,15188],{"style":2750},[50,15186],{"className":15187,"style":2754},[211],[50,15189,15191],{"className":15190,"style":3803},[2804],[50,15192,2599],{"className":15193},[182],[50,15195,227],{"className":15196},[226],[50,15198,15200],{"className":15199},[199],[50,15201,15203],{"className":15202,"style":2818},[203],[50,15204],{},[50,15206,100],{"className":15207},[291],[50,15209],{"className":15210,"style":736},[244],[50,15212,1080],{"className":15213},[1212],[50,15215],{"className":15216,"style":736},[244],[50,15218,15220,15223,15226,15229,15232,15235],{"className":15219},[168],[50,15221],{"className":15222,"style":173},[172],[50,15224,75],{"className":15225},[177],[50,15227,86],{"className":15228},[182],[50,15230],{"className":15231,"style":736},[244],[50,15233,2587],{"className":15234},[1212],[50,15236],{"className":15237,"style":736},[244],[50,15239,15241,15244,15247,15250,15253,15256],{"className":15240},[168],[50,15242],{"className":15243,"style":173},[172],[50,15245,95],{"className":15246,"style":252},[182,186],[50,15248,100],{"className":15249},[291],[50,15251],{"className":15252,"style":736},[244],[50,15254,15046],{"className":15255},[1212],[50,15257],{"className":15258,"style":736},[244],[50,15260,15262,15265,15270,15273,15276,15279,15282],{"className":15261},[168],[50,15263],{"className":15264,"style":173},[172],[50,15266,15155,15268],{"className":15267},[3356],[50,15269,15159],{"style":15158},[50,15271,75],{"className":15272},[177],[50,15274,86],{"className":15275},[182],[50,15277],{"className":15278,"style":736},[244],[50,15280,2587],{"className":15281},[1212],[50,15283],{"className":15284,"style":736},[244],[50,15286,15288,15291,15333],{"className":15287},[168],[50,15289],{"className":15290,"style":173},[172],[50,15292,15294],{"className":15293},[182,2737],[50,15295,15297,15325],{"className":15296},[194,195],[50,15298,15300,15322],{"className":15299},[199],[50,15301,15303,15311],{"className":15302,"style":3501},[203],[50,15304,15305,15308],{"style":2750},[50,15306],{"className":15307,"style":2754},[211],[50,15309,95],{"className":15310,"style":252},[182,186],[50,15312,15313,15316],{"style":2750},[50,15314],{"className":15315,"style":2754},[211],[50,15317,15319],{"className":15318,"style":3803},[2804],[50,15320,2599],{"className":15321},[182],[50,15323,227],{"className":15324},[226],[50,15326,15328],{"className":15327},[199],[50,15329,15331],{"className":15330,"style":2818},[203],[50,15332],{},[50,15334,15336],{"className":15335},[291],")]",[11,15338,15339],{},"Average loss (or cost function) for the entire dataset:",[50,15341,15343],{"className":15342},[650],[50,15344,15346,15455],{"className":15345},[53],[50,15347,15349],{"className":15348},[57],[59,15350,15351],{"xmlns":61,"display":659},[63,15352,15353,15452],{},[66,15354,15355,15358,15360,15362,15368,15382,15384,15390,15392,15394,15396,15398,15408,15410,15412,15414,15416,15418,15424,15426,15428,15430,15432,15434,15436,15438,15448,15450],{},[80,15356,15357],{},"J",[69,15359,1069],{},[69,15361,2587],{},[3192,15363,15364,15366],{},[84,15365,86],{},[80,15367,142],{},[3199,15369,15370,15372,15380],{},[69,15371,3203],{},[66,15373,15374,15376,15378],{},[80,15375,519],{},[69,15377,1069],{},[84,15379,86],{},[80,15381,142],{},[69,15383,15041],{"stretchy":71},[77,15385,15386,15388],{},[80,15387,95],{},[80,15389,519],{},[69,15391,15046],{},[80,15393,15049],{},[69,15395,15052],{},[69,15397,75],{"stretchy":71},[2589,15399,15400,15406],{"accent":89},[77,15401,15402,15404],{},[80,15403,95],{},[80,15405,519],{},[69,15407,2599],{},[69,15409,100],{"stretchy":71},[69,15411,1080],{},[69,15413,75],{"stretchy":71},[84,15415,86],{},[69,15417,2587],{},[77,15419,15420,15422],{},[80,15421,95],{},[80,15423,519],{},[69,15425,100],{"stretchy":71},[69,15427,15046],{},[80,15429,15049],{},[69,15431,15052],{},[69,15433,75],{"stretchy":71},[84,15435,86],{},[69,15437,2587],{},[2589,15439,15440,15446],{"accent":89},[77,15441,15442,15444],{},[80,15443,95],{},[80,15445,519],{},[69,15447,2599],{},[69,15449,100],{"stretchy":71},[69,15451,15097],{"stretchy":71},[157,15453,15454],{"encoding":159},"J = -\\frac{1}{n} \\sum_{i=1}^{n} [y_i \\cdot \\log(\\hat{y_i}) + (1 - y_i) \\cdot \\log(1 - \\hat{y_i})]",[50,15456,15458,15477,15670,15775,15796,15854,15880],{"className":15457,"ariaHidden":89},[164],[50,15459,15461,15464,15468,15471,15474],{"className":15460},[168],[50,15462],{"className":15463,"style":713},[172],[50,15465,15357],{"className":15466,"style":15467},[182,186],"margin-right:0.0962em;",[50,15469],{"className":15470,"style":699},[244],[50,15472,1069],{"className":15473},[703],[50,15475],{"className":15476,"style":699},[244],[50,15478,15480,15483,15486,15548,15551,15618,15621,15661,15664,15667],{"className":15479},[168],[50,15481],{"className":15482,"style":3279},[172],[50,15484,2587],{"className":15485},[182],[50,15487,15489,15492,15545],{"className":15488},[182],[50,15490],{"className":15491},[177,3286],[50,15493,15495],{"className":15494},[3192],[50,15496,15498,15537],{"className":15497},[194,195],[50,15499,15501,15534],{"className":15500},[199],[50,15502,15504,15515,15523],{"className":15503,"style":3299},[203],[50,15505,15506,15509],{"style":3302},[50,15507],{"className":15508,"style":2754},[211],[50,15510,15512],{"className":15511},[182],[50,15513,142],{"className":15514},[182,186],[50,15516,15517,15520],{"style":3314},[50,15518],{"className":15519,"style":2754},[211],[50,15521],{"className":15522,"style":3322},[3321],[50,15524,15525,15528],{"style":3325},[50,15526],{"className":15527,"style":2754},[211],[50,15529,15531],{"className":15530},[182],[50,15532,86],{"className":15533},[182],[50,15535,227],{"className":15536},[226],[50,15538,15540],{"className":15539},[199],[50,15541,15543],{"className":15542,"style":3344},[203],[50,15544],{},[50,15546],{"className":15547},[291,3286],[50,15549],{"className":15550,"style":245},[244],[50,15552,15554],{"className":15553},[3356,3357],[50,15555,15557,15610],{"className":15556},[194,195],[50,15558,15560,15607],{"className":15559},[199],[50,15561,15563,15583,15593],{"className":15562,"style":3367},[203],[50,15564,15565,15568],{"style":3370},[50,15566],{"className":15567,"style":3374},[211],[50,15569,15571],{"className":15570},[216,217,218,219],[50,15572,15574,15577,15580],{"className":15573},[182,219],[50,15575,519],{"className":15576},[182,186,219],[50,15578,1069],{"className":15579},[703,219],[50,15581,86],{"className":15582},[182,219],[50,15584,15585,15588],{"style":3392},[50,15586],{"className":15587,"style":3374},[211],[50,15589,15590],{},[50,15591,3203],{"className":15592},[3356,3401,3402],[50,15594,15595,15598],{"style":3405},[50,15596],{"className":15597,"style":3374},[211],[50,15599,15601],{"className":15600},[216,217,218,219],[50,15602,15604],{"className":15603},[182,219],[50,15605,142],{"className":15606},[182,186,219],[50,15608,227],{"className":15609},[226],[50,15611,15613],{"className":15612},[199],[50,15614,15616],{"className":15615,"style":3427},[203],[50,15617],{},[50,15619,15041],{"className":15620},[177],[50,15622,15624,15627],{"className":15623},[182],[50,15625,95],{"className":15626,"style":252},[182,186],[50,15628,15630],{"className":15629},[190],[50,15631,15633,15653],{"className":15632},[194,195],[50,15634,15636,15650],{"className":15635},[199],[50,15637,15639],{"className":15638,"style":551},[203],[50,15640,15641,15644],{"style":267},[50,15642],{"className":15643,"style":212},[211],[50,15645,15647],{"className":15646},[216,217,218,219],[50,15648,519],{"className":15649},[182,186,219],[50,15651,227],{"className":15652},[226],[50,15654,15656],{"className":15655},[199],[50,15657,15659],{"className":15658,"style":234},[203],[50,15660],{},[50,15662],{"className":15663,"style":736},[244],[50,15665,15046],{"className":15666},[1212],[50,15668],{"className":15669,"style":736},[244],[50,15671,15673,15676,15681,15684,15763,15766,15769,15772],{"className":15672},[168],[50,15674],{"className":15675,"style":173},[172],[50,15677,15155,15679],{"className":15678},[3356],[50,15680,15159],{"style":15158},[50,15682,75],{"className":15683},[177],[50,15685,15687],{"className":15686},[182,2737],[50,15688,15690,15755],{"className":15689},[194,195],[50,15691,15693,15752],{"className":15692},[199],[50,15694,15696,15741],{"className":15695,"style":3501},[203],[50,15697,15698,15701],{"style":2750},[50,15699],{"className":15700,"style":2754},[211],[50,15702,15704,15707],{"className":15703},[182],[50,15705,95],{"className":15706,"style":252},[182,186],[50,15708,15710],{"className":15709},[190],[50,15711,15713,15733],{"className":15712},[194,195],[50,15714,15716,15730],{"className":15715},[199],[50,15717,15719],{"className":15718,"style":551},[203],[50,15720,15721,15724],{"style":267},[50,15722],{"className":15723,"style":212},[211],[50,15725,15727],{"className":15726},[216,217,218,219],[50,15728,519],{"className":15729},[182,186,219],[50,15731,227],{"className":15732},[226],[50,15734,15736],{"className":15735},[199],[50,15737,15739],{"className":15738,"style":234},[203],[50,15740],{},[50,15742,15743,15746],{"style":2750},[50,15744],{"className":15745,"style":2754},[211],[50,15747,15749],{"className":15748,"style":2805},[2804],[50,15750,2599],{"className":15751},[182],[50,15753,227],{"className":15754},[226],[50,15756,15758],{"className":15757},[199],[50,15759,15761],{"className":15760,"style":2818},[203],[50,15762],{},[50,15764,100],{"className":15765},[291],[50,15767],{"className":15768,"style":736},[244],[50,15770,1080],{"className":15771},[1212],[50,15773],{"className":15774,"style":736},[244],[50,15776,15778,15781,15784,15787,15790,15793],{"className":15777},[168],[50,15779],{"className":15780,"style":173},[172],[50,15782,75],{"className":15783},[177],[50,15785,86],{"className":15786},[182],[50,15788],{"className":15789,"style":736},[244],[50,15791,2587],{"className":15792},[1212],[50,15794],{"className":15795,"style":736},[244],[50,15797,15799,15802,15842,15845,15848,15851],{"className":15798},[168],[50,15800],{"className":15801,"style":173},[172],[50,15803,15805,15808],{"className":15804},[182],[50,15806,95],{"className":15807,"style":252},[182,186],[50,15809,15811],{"className":15810},[190],[50,15812,15814,15834],{"className":15813},[194,195],[50,15815,15817,15831],{"className":15816},[199],[50,15818,15820],{"className":15819,"style":551},[203],[50,15821,15822,15825],{"style":267},[50,15823],{"className":15824,"style":212},[211],[50,15826,15828],{"className":15827},[216,217,218,219],[50,15829,519],{"className":15830},[182,186,219],[50,15832,227],{"className":15833},[226],[50,15835,15837],{"className":15836},[199],[50,15838,15840],{"className":15839,"style":234},[203],[50,15841],{},[50,15843,100],{"className":15844},[291],[50,15846],{"className":15847,"style":736},[244],[50,15849,15046],{"className":15850},[1212],[50,15852],{"className":15853,"style":736},[244],[50,15855,15857,15860,15865,15868,15871,15874,15877],{"className":15856},[168],[50,15858],{"className":15859,"style":173},[172],[50,15861,15155,15863],{"className":15862},[3356],[50,15864,15159],{"style":15158},[50,15866,75],{"className":15867},[177],[50,15869,86],{"className":15870},[182],[50,15872],{"className":15873,"style":736},[244],[50,15875,2587],{"className":15876},[1212],[50,15878],{"className":15879,"style":736},[244],[50,15881,15883,15886,15965],{"className":15882},[168],[50,15884],{"className":15885,"style":173},[172],[50,15887,15889],{"className":15888},[182,2737],[50,15890,15892,15957],{"className":15891},[194,195],[50,15893,15895,15954],{"className":15894},[199],[50,15896,15898,15943],{"className":15897,"style":3501},[203],[50,15899,15900,15903],{"style":2750},[50,15901],{"className":15902,"style":2754},[211],[50,15904,15906,15909],{"className":15905},[182],[50,15907,95],{"className":15908,"style":252},[182,186],[50,15910,15912],{"className":15911},[190],[50,15913,15915,15935],{"className":15914},[194,195],[50,15916,15918,15932],{"className":15917},[199],[50,15919,15921],{"className":15920,"style":551},[203],[50,15922,15923,15926],{"style":267},[50,15924],{"className":15925,"style":212},[211],[50,15927,15929],{"className":15928},[216,217,218,219],[50,15930,519],{"className":15931},[182,186,219],[50,15933,227],{"className":15934},[226],[50,15936,15938],{"className":15937},[199],[50,15939,15941],{"className":15940,"style":234},[203],[50,15942],{},[50,15944,15945,15948],{"style":2750},[50,15946],{"className":15947,"style":2754},[211],[50,15949,15951],{"className":15950,"style":2805},[2804],[50,15952,2599],{"className":15953},[182],[50,15955,227],{"className":15956},[226],[50,15958,15960],{"className":15959},[199],[50,15961,15963],{"className":15962,"style":2818},[203],[50,15964],{},[50,15966,15336],{"className":15967},[291],[11,15969,1534],{},[741,15971,15972,16002,16102],{},[744,15973,15974,3634],{},[50,15975,15977,15990],{"className":15976},[53],[50,15978,15980],{"className":15979},[57],[59,15981,15982],{"xmlns":61},[63,15983,15984,15988],{},[66,15985,15986],{},[80,15987,142],{},[157,15989,142],{"encoding":159},[50,15991,15993],{"className":15992,"ariaHidden":89},[164],[50,15994,15996,15999],{"className":15995},[168],[50,15997],{"className":15998,"style":1528},[172],[50,16000,142],{"className":16001},[182,186],[744,16003,16004,16073,16074,127],{},[50,16005,16007,16024],{"className":16006},[53],[50,16008,16010],{"className":16009},[57],[59,16011,16012],{"xmlns":61},[63,16013,16014,16022],{},[66,16015,16016],{},[77,16017,16018,16020],{},[80,16019,95],{},[80,16021,519],{},[157,16023,595],{"encoding":159},[50,16025,16027],{"className":16026,"ariaHidden":89},[164],[50,16028,16030,16033],{"className":16029},[168],[50,16031],{"className":16032,"style":605},[172],[50,16034,16036,16039],{"className":16035},[182],[50,16037,95],{"className":16038,"style":252},[182,186],[50,16040,16042],{"className":16041},[190],[50,16043,16045,16065],{"className":16044},[194,195],[50,16046,16048,16062],{"className":16047},[199],[50,16049,16051],{"className":16050,"style":551},[203],[50,16052,16053,16056],{"style":267},[50,16054],{"className":16055,"style":212},[211],[50,16057,16059],{"className":16058},[216,217,218,219],[50,16060,519],{"className":16061},[182,186,219],[50,16063,227],{"className":16064},[226],[50,16066,16068],{"className":16067},[199],[50,16069,16071],{"className":16070,"style":234},[203],[50,16072],{}," is the true label (0 or 1) for example ",[50,16075,16077,16090],{"className":16076},[53],[50,16078,16080],{"className":16079},[57],[59,16081,16082],{"xmlns":61},[63,16083,16084,16088],{},[66,16085,16086],{},[80,16087,519],{},[157,16089,519],{"encoding":159},[50,16091,16093],{"className":16092,"ariaHidden":89},[164],[50,16094,16096,16099],{"className":16095},[168],[50,16097],{"className":16098,"style":3732},[172],[50,16100,519],{"className":16101},[182,186],[744,16103,16104,16217,16218,16246],{},[50,16105,16107,16129],{"className":16106},[53],[50,16108,16110],{"className":16109},[57],[59,16111,16112],{"xmlns":61},[63,16113,16114,16126],{},[66,16115,16116],{},[2589,16117,16118,16124],{"accent":89},[77,16119,16120,16122],{},[80,16121,95],{},[80,16123,519],{},[69,16125,2599],{},[157,16127,16128],{"encoding":159},"\\hat{y_i}",[50,16130,16132],{"className":16131,"ariaHidden":89},[164],[50,16133,16135,16138],{"className":16134},[168],[50,16136],{"className":16137,"style":691},[172],[50,16139,16141],{"className":16140},[182,2737],[50,16142,16144,16209],{"className":16143},[194,195],[50,16145,16147,16206],{"className":16146},[199],[50,16148,16150,16195],{"className":16149,"style":3501},[203],[50,16151,16152,16155],{"style":2750},[50,16153],{"className":16154,"style":2754},[211],[50,16156,16158,16161],{"className":16157},[182],[50,16159,95],{"className":16160,"style":252},[182,186],[50,16162,16164],{"className":16163},[190],[50,16165,16167,16187],{"className":16166},[194,195],[50,16168,16170,16184],{"className":16169},[199],[50,16171,16173],{"className":16172,"style":551},[203],[50,16174,16175,16178],{"style":267},[50,16176],{"className":16177,"style":212},[211],[50,16179,16181],{"className":16180},[216,217,218,219],[50,16182,519],{"className":16183},[182,186,219],[50,16185,227],{"className":16186},[226],[50,16188,16190],{"className":16189},[199],[50,16191,16193],{"className":16192,"style":234},[203],[50,16194],{},[50,16196,16197,16200],{"style":2750},[50,16198],{"className":16199,"style":2754},[211],[50,16201,16203],{"className":16202,"style":2805},[2804],[50,16204,2599],{"className":16205},[182],[50,16207,227],{"className":16208},[226],[50,16210,16212],{"className":16211},[199],[50,16213,16215],{"className":16214,"style":2818},[203],[50,16216],{}," is the predicted probability by the model for example ",[50,16219,16221,16234],{"className":16220},[53],[50,16222,16224],{"className":16223},[57],[59,16225,16226],{"xmlns":61},[63,16227,16228,16232],{},[66,16229,16230],{},[80,16231,519],{},[157,16233,519],{"encoding":159},[50,16235,16237],{"className":16236,"ariaHidden":89},[164],[50,16238,16240,16243],{"className":16239},[168],[50,16241],{"className":16242,"style":3732},[172],[50,16244,519],{"className":16245},[182,186]," (value between 0 and 1).",[11,16248,16249],{},"Why is cross-entropy used?",[996,16251,16252,16255,16258],{},[744,16253,16254],{},"It penalizes incorrect predictions with high confidence more heavily.",[744,16256,16257],{},"It is a convex loss function, which makes optimization easier using methods like gradient descent.",[744,16259,16260],{},"It has a probabilistic interpretation, as it is based on the predicted probability by the model.",[11,16262,16263],{},"The behavior of the loss function is shown in the following graph:",[11,16265,16266,16270],{},[2432,16267],{"alt":16268,"src":16269},"Cross-Entropy Loss Function Graph","\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations\u002Fshared\u002Fcross-entropy.webp",[2437,16271,16268],{},[11,16273,16274],{},"The function penalizes more heavily the incorrect predictions with high confidence, which is reflected in the shape of the curve.",[996,16276,16277],{},[744,16278,16279],{},"When the true label is 1 (y=1) (blue line):",[741,16281,16282,16406,16481],{},[744,16283,16284,16285],{},"The formula simplifies to ",[50,16286,16288,16320],{"className":16287},[53],[50,16289,16291],{"className":16290},[57],[59,16292,16293],{"xmlns":61},[63,16294,16295,16317],{},[66,16296,16297,16299,16301,16303,16305,16307,16309,16315],{},[80,16298,15034],{},[69,16300,1069],{},[69,16302,2587],{},[80,16304,15049],{},[69,16306,15052],{},[69,16308,75],{"stretchy":71},[2589,16310,16311,16313],{"accent":89},[80,16312,95],{},[69,16314,2599],{},[69,16316,100],{"stretchy":71},[157,16318,16319],{"encoding":159},"L = -\\log(\\hat{y})",[50,16321,16323,16341],{"className":16322,"ariaHidden":89},[164],[50,16324,16326,16329,16332,16335,16338],{"className":16325},[168],[50,16327],{"className":16328,"style":713},[172],[50,16330,15034],{"className":16331},[182,186],[50,16333],{"className":16334,"style":699},[244],[50,16336,1069],{"className":16337},[703],[50,16339],{"className":16340,"style":699},[244],[50,16342,16344,16347,16350,16353,16358,16361,16403],{"className":16343},[168],[50,16345],{"className":16346,"style":173},[172],[50,16348,2587],{"className":16349},[182],[50,16351],{"className":16352,"style":245},[244],[50,16354,15155,16356],{"className":16355},[3356],[50,16357,15159],{"style":15158},[50,16359,75],{"className":16360},[177],[50,16362,16364],{"className":16363},[182,2737],[50,16365,16367,16395],{"className":16366},[194,195],[50,16368,16370,16392],{"className":16369},[199],[50,16371,16373,16381],{"className":16372,"style":3501},[203],[50,16374,16375,16378],{"style":2750},[50,16376],{"className":16377,"style":2754},[211],[50,16379,95],{"className":16380,"style":252},[182,186],[50,16382,16383,16386],{"style":2750},[50,16384],{"className":16385,"style":2754},[211],[50,16387,16389],{"className":16388,"style":3803},[2804],[50,16390,2599],{"className":16391},[182],[50,16393,227],{"className":16394},[226],[50,16396,16398],{"className":16397},[199],[50,16399,16401],{"className":16400,"style":2818},[203],[50,16402],{},[50,16404,100],{"className":16405},[291],[744,16407,14560,16408,16480],{},[50,16409,16411,16429],{"className":16410},[53],[50,16412,16414],{"className":16413},[57],[59,16415,16416],{"xmlns":61},[63,16417,16418,16426],{},[66,16419,16420],{},[2589,16421,16422,16424],{"accent":89},[80,16423,95],{},[69,16425,2599],{},[157,16427,16428],{"encoding":159},"\\hat{y}",[50,16430,16432],{"className":16431,"ariaHidden":89},[164],[50,16433,16435,16438],{"className":16434},[168],[50,16436],{"className":16437,"style":691},[172],[50,16439,16441],{"className":16440},[182,2737],[50,16442,16444,16472],{"className":16443},[194,195],[50,16445,16447,16469],{"className":16446},[199],[50,16448,16450,16458],{"className":16449,"style":3501},[203],[50,16451,16452,16455],{"style":2750},[50,16453],{"className":16454,"style":2754},[211],[50,16456,95],{"className":16457,"style":252},[182,186],[50,16459,16460,16463],{"style":2750},[50,16461],{"className":16462,"style":2754},[211],[50,16464,16466],{"className":16465,"style":3803},[2804],[50,16467,2599],{"className":16468},[182],[50,16470,227],{"className":16471},[226],[50,16473,16475],{"className":16474},[199],[50,16476,16478],{"className":16477,"style":2818},[203],[50,16479],{}," approaches 1, the loss approaches 0 (good prediction).",[744,16482,14560,16483,16554],{},[50,16484,16486,16503],{"className":16485},[53],[50,16487,16489],{"className":16488},[57],[59,16490,16491],{"xmlns":61},[63,16492,16493,16501],{},[66,16494,16495],{},[2589,16496,16497,16499],{"accent":89},[80,16498,95],{},[69,16500,2599],{},[157,16502,16428],{"encoding":159},[50,16504,16506],{"className":16505,"ariaHidden":89},[164],[50,16507,16509,16512],{"className":16508},[168],[50,16510],{"className":16511,"style":691},[172],[50,16513,16515],{"className":16514},[182,2737],[50,16516,16518,16546],{"className":16517},[194,195],[50,16519,16521,16543],{"className":16520},[199],[50,16522,16524,16532],{"className":16523,"style":3501},[203],[50,16525,16526,16529],{"style":2750},[50,16527],{"className":16528,"style":2754},[211],[50,16530,95],{"className":16531,"style":252},[182,186],[50,16533,16534,16537],{"style":2750},[50,16535],{"className":16536,"style":2754},[211],[50,16538,16540],{"className":16539,"style":3803},[2804],[50,16541,2599],{"className":16542},[182],[50,16544,227],{"className":16545},[226],[50,16547,16549],{"className":16548},[199],[50,16550,16552],{"className":16551,"style":2818},[203],[50,16553],{}," approaches 0, the loss shoots to infinity (bad prediction).",[996,16556,16557],{"start":10446},[744,16558,16559],{},"When the true label is 0 (y=0) (red line):",[741,16561,16562,16707,16781],{},[744,16563,16284,16564],{},[50,16565,16567,16603],{"className":16566},[53],[50,16568,16570],{"className":16569},[57],[59,16571,16572],{"xmlns":61},[63,16573,16574,16600],{},[66,16575,16576,16578,16580,16582,16584,16586,16588,16590,16592,16598],{},[80,16577,15034],{},[69,16579,1069],{},[69,16581,2587],{},[80,16583,15049],{},[69,16585,15052],{},[69,16587,75],{"stretchy":71},[84,16589,86],{},[69,16591,2587],{},[2589,16593,16594,16596],{"accent":89},[80,16595,95],{},[69,16597,2599],{},[69,16599,100],{"stretchy":71},[157,16601,16602],{"encoding":159},"L = -\\log(1 - \\hat{y})",[50,16604,16606,16624,16656],{"className":16605,"ariaHidden":89},[164],[50,16607,16609,16612,16615,16618,16621],{"className":16608},[168],[50,16610],{"className":16611,"style":713},[172],[50,16613,15034],{"className":16614},[182,186],[50,16616],{"className":16617,"style":699},[244],[50,16619,1069],{"className":16620},[703],[50,16622],{"className":16623,"style":699},[244],[50,16625,16627,16630,16633,16636,16641,16644,16647,16650,16653],{"className":16626},[168],[50,16628],{"className":16629,"style":173},[172],[50,16631,2587],{"className":16632},[182],[50,16634],{"className":16635,"style":245},[244],[50,16637,15155,16639],{"className":16638},[3356],[50,16640,15159],{"style":15158},[50,16642,75],{"className":16643},[177],[50,16645,86],{"className":16646},[182],[50,16648],{"className":16649,"style":736},[244],[50,16651,2587],{"className":16652},[1212],[50,16654],{"className":16655,"style":736},[244],[50,16657,16659,16662,16704],{"className":16658},[168],[50,16660],{"className":16661,"style":173},[172],[50,16663,16665],{"className":16664},[182,2737],[50,16666,16668,16696],{"className":16667},[194,195],[50,16669,16671,16693],{"className":16670},[199],[50,16672,16674,16682],{"className":16673,"style":3501},[203],[50,16675,16676,16679],{"style":2750},[50,16677],{"className":16678,"style":2754},[211],[50,16680,95],{"className":16681,"style":252},[182,186],[50,16683,16684,16687],{"style":2750},[50,16685],{"className":16686,"style":2754},[211],[50,16688,16690],{"className":16689,"style":3803},[2804],[50,16691,2599],{"className":16692},[182],[50,16694,227],{"className":16695},[226],[50,16697,16699],{"className":16698},[199],[50,16700,16702],{"className":16701,"style":2818},[203],[50,16703],{},[50,16705,100],{"className":16706},[291],[744,16708,14560,16709,16780],{},[50,16710,16712,16729],{"className":16711},[53],[50,16713,16715],{"className":16714},[57],[59,16716,16717],{"xmlns":61},[63,16718,16719,16727],{},[66,16720,16721],{},[2589,16722,16723,16725],{"accent":89},[80,16724,95],{},[69,16726,2599],{},[157,16728,16428],{"encoding":159},[50,16730,16732],{"className":16731,"ariaHidden":89},[164],[50,16733,16735,16738],{"className":16734},[168],[50,16736],{"className":16737,"style":691},[172],[50,16739,16741],{"className":16740},[182,2737],[50,16742,16744,16772],{"className":16743},[194,195],[50,16745,16747,16769],{"className":16746},[199],[50,16748,16750,16758],{"className":16749,"style":3501},[203],[50,16751,16752,16755],{"style":2750},[50,16753],{"className":16754,"style":2754},[211],[50,16756,95],{"className":16757,"style":252},[182,186],[50,16759,16760,16763],{"style":2750},[50,16761],{"className":16762,"style":2754},[211],[50,16764,16766],{"className":16765,"style":3803},[2804],[50,16767,2599],{"className":16768},[182],[50,16770,227],{"className":16771},[226],[50,16773,16775],{"className":16774},[199],[50,16776,16778],{"className":16777,"style":2818},[203],[50,16779],{}," approaches 0, the loss approaches 0 (good prediction).",[744,16782,14560,16783,16854],{},[50,16784,16786,16803],{"className":16785},[53],[50,16787,16789],{"className":16788},[57],[59,16790,16791],{"xmlns":61},[63,16792,16793,16801],{},[66,16794,16795],{},[2589,16796,16797,16799],{"accent":89},[80,16798,95],{},[69,16800,2599],{},[157,16802,16428],{"encoding":159},[50,16804,16806],{"className":16805,"ariaHidden":89},[164],[50,16807,16809,16812],{"className":16808},[168],[50,16810],{"className":16811,"style":691},[172],[50,16813,16815],{"className":16814},[182,2737],[50,16816,16818,16846],{"className":16817},[194,195],[50,16819,16821,16843],{"className":16820},[199],[50,16822,16824,16832],{"className":16823,"style":3501},[203],[50,16825,16826,16829],{"style":2750},[50,16827],{"className":16828,"style":2754},[211],[50,16830,95],{"className":16831,"style":252},[182,186],[50,16833,16834,16837],{"style":2750},[50,16835],{"className":16836,"style":2754},[211],[50,16838,16840],{"className":16839,"style":3803},[2804],[50,16841,2599],{"className":16842},[182],[50,16844,227],{"className":16845},[226],[50,16847,16849],{"className":16848},[199],[50,16850,16852],{"className":16851,"style":2818},[203],[50,16853],{}," approaches 1, the loss shoots to infinity (bad prediction).",[11,16856,16857],{},"The logarithmic nature of the function ensures that the model is heavily penalized when it is \"confident but wrong\", forcing it to adjust its weights more aggressively to improve predictions.",[31,16859,16861],{"id":16860},"validation-and-optimization","Validation and Optimization",[39,16863,16865],{"id":16864},"evaluation-metrics","Evaluation Metrics",[11,16867,16868,16869,16871],{},"For ",[747,16870,1043],{},", the common metrics include:",[741,16873,16874,16880,16886],{},[744,16875,16876,16879],{},[747,16877,16878],{},"Mean Squared Error (MSE)",": Average of the squares of the differences between the actual values and the predictions.",[744,16881,16882,16885],{},[747,16883,16884],{},"Root Mean Squared Error (RMSE)",": Square root of the MSE, which has the same unit as the dependent variable.",[744,16887,16888,16891],{},[747,16889,16890],{},"Coefficient of Determination (R²)",": Proportion of the variance in the dependent variable that is explained by the model.",[11,16893,16868,16894,16871],{},[747,16895,11468],{},[741,16897,16898,16904,16910,16916],{},[744,16899,16900,16903],{},[747,16901,16902],{},"Accuracy",": Proportion of correct predictions over the total number of examples.",[744,16905,16906,16909],{},[747,16907,16908],{},"Precision",": Proportion of true positives over the total number of positive predictions.",[744,16911,16912,16915],{},[747,16913,16914],{},"Recall (Sensitivity)",": Proportion of true positives over the total number of actual positive examples.",[744,16917,16918,16921],{},[747,16919,16920],{},"F1 Score",": Harmonic mean of precision and recall, providing a balanced measure between both.",[4606,16923,16924],{},[11,16925,16926],{},"The choice of the appropriate metric depends on the context of the problem and the consequences of classification errors. For example, in a fraud detection problem, it is more important to minimize false negatives (failing to detect fraud) than false positives (marking a legitimate transaction as fraud), so recall could be a more relevant metric than precision.",[39,16928,16930],{"id":16929},"optimization","Optimization",[11,16932,16933,16934,16937],{},"Optimization of machine learning models refers to the process of adjusting the model parameters to ",[747,16935,16936],{},"minimize the loss function",". This can be achieved through techniques like gradient descent, which iteratively adjusts the model weights in the direction that reduces the loss.",[11,16939,16940],{},"Gradient descent can be mathematically expressed as:",[50,16942,16944],{"className":16943},[650],[50,16945,16947,16982],{"className":16946},[53],[50,16948,16950],{"className":16949},[57],[59,16951,16952],{"xmlns":61,"display":659},[63,16953,16954,16979],{},[66,16955,16956,16959,16961,16963,16965,16968,16971,16973,16975,16977],{},[80,16957,16958],{},"θ",[69,16960,1069],{},[80,16962,16958],{},[69,16964,2587],{},[80,16966,16967],{},"α",[80,16969,16970],{"mathvariant":126},"∇",[80,16972,15357],{},[69,16974,75],{"stretchy":71},[80,16976,16958],{},[69,16978,100],{"stretchy":71},[157,16980,16981],{"encoding":159},"\\theta = \\theta - \\alpha \\nabla J(\\theta)",[50,16983,16985,17004,17023],{"className":16984,"ariaHidden":89},[164],[50,16986,16988,16991,16995,16998,17001],{"className":16987},[168],[50,16989],{"className":16990,"style":3501},[172],[50,16992,16958],{"className":16993,"style":16994},[182,186],"margin-right:0.0278em;",[50,16996],{"className":16997,"style":699},[244],[50,16999,1069],{"className":17000},[703],[50,17002],{"className":17003,"style":699},[244],[50,17005,17007,17011,17014,17017,17020],{"className":17006},[168],[50,17008],{"className":17009,"style":17010},[172],"height:0.7778em;vertical-align:-0.0833em;",[50,17012,16958],{"className":17013,"style":16994},[182,186],[50,17015],{"className":17016,"style":736},[244],[50,17018,2587],{"className":17019},[1212],[50,17021],{"className":17022,"style":736},[244],[50,17024,17026,17029,17033,17036,17039,17042,17045],{"className":17025},[168],[50,17027],{"className":17028,"style":173},[172],[50,17030,16967],{"className":17031,"style":17032},[182,186],"margin-right:0.0037em;",[50,17034,16970],{"className":17035},[182],[50,17037,15357],{"className":17038,"style":15467},[182,186],[50,17040,75],{"className":17041},[177],[50,17043,16958],{"className":17044,"style":16994},[182,186],[50,17046,100],{"className":17047},[291],[11,17049,1534],{},[741,17051,17052,17084,17116],{},[744,17053,17054,17083],{},[50,17055,17057,17071],{"className":17056},[53],[50,17058,17060],{"className":17059},[57],[59,17061,17062],{"xmlns":61},[63,17063,17064,17068],{},[66,17065,17066],{},[80,17067,16958],{},[157,17069,17070],{"encoding":159},"\\theta",[50,17072,17074],{"className":17073,"ariaHidden":89},[164],[50,17075,17077,17080],{"className":17076},[168],[50,17078],{"className":17079,"style":3501},[172],[50,17081,16958],{"className":17082,"style":16994},[182,186]," represents the model parameters (for example, the coefficients in regression).",[744,17085,17086,17115],{},[50,17087,17089,17103],{"className":17088},[53],[50,17090,17092],{"className":17091},[57],[59,17093,17094],{"xmlns":61},[63,17095,17096,17100],{},[66,17097,17098],{},[80,17099,16967],{},[157,17101,17102],{"encoding":159},"\\alpha",[50,17104,17106],{"className":17105,"ariaHidden":89},[164],[50,17107,17109,17112],{"className":17108},[168],[50,17110],{"className":17111,"style":1528},[172],[50,17113,16967],{"className":17114,"style":17032},[182,186]," is the learning rate, which controls the size of the steps taken in each iteration.",[744,17117,17118,17167],{},[50,17119,17121,17143],{"className":17120},[53],[50,17122,17124],{"className":17123},[57],[59,17125,17126],{"xmlns":61},[63,17127,17128,17140],{},[66,17129,17130,17132,17134,17136,17138],{},[80,17131,16970],{"mathvariant":126},[80,17133,15357],{},[69,17135,75],{"stretchy":71},[80,17137,16958],{},[69,17139,100],{"stretchy":71},[157,17141,17142],{"encoding":159},"\\nabla J(\\theta)",[50,17144,17146],{"className":17145,"ariaHidden":89},[164],[50,17147,17149,17152,17155,17158,17161,17164],{"className":17148},[168],[50,17150],{"className":17151,"style":173},[172],[50,17153,16970],{"className":17154},[182],[50,17156,15357],{"className":17157,"style":15467},[182,186],[50,17159,75],{"className":17160},[177],[50,17162,16958],{"className":17163,"style":16994},[182,186],[50,17165,100],{"className":17166},[291]," is the gradient of the loss function with respect to the parameters, indicating the direction of greatest increase in the loss.",[11,17169,17170],{},"The optimization process continues until a convergence criterion is met, such as a maximum number of iterations or a minimum improvement in the loss function.",[741,17172,17173,17209,17263],{},[744,17174,17175,17176,17179,17180,17208],{},"It has a ",[747,17177,17178],{},"learning rate"," (",[50,17181,17183,17196],{"className":17182},[53],[50,17184,17186],{"className":17185},[57],[59,17187,17188],{"xmlns":61},[63,17189,17190,17194],{},[66,17191,17192],{},[80,17193,16967],{},[157,17195,17102],{"encoding":159},[50,17197,17199],{"className":17198,"ariaHidden":89},[164],[50,17200,17202,17205],{"className":17201},[168],[50,17203],{"className":17204,"style":1528},[172],[50,17206,16967],{"className":17207,"style":17032},[182,186],") that controls the size of the steps taken in each iteration (typically a small value like 0.01 or 0.001).",[744,17210,2221,17211,17179,17214,17262],{},[747,17212,17213],{},"gradient",[50,17215,17217,17238],{"className":17216},[53],[50,17218,17220],{"className":17219},[57],[59,17221,17222],{"xmlns":61},[63,17223,17224,17236],{},[66,17225,17226,17228,17230,17232,17234],{},[80,17227,16970],{"mathvariant":126},[80,17229,15357],{},[69,17231,75],{"stretchy":71},[80,17233,16958],{},[69,17235,100],{"stretchy":71},[157,17237,17142],{"encoding":159},[50,17239,17241],{"className":17240,"ariaHidden":89},[164],[50,17242,17244,17247,17250,17253,17256,17259],{"className":17243},[168],[50,17245],{"className":17246,"style":173},[172],[50,17248,16970],{"className":17249},[182],[50,17251,15357],{"className":17252,"style":15467},[182,186],[50,17254,75],{"className":17255},[177],[50,17257,16958],{"className":17258,"style":16994},[182,186],[50,17260,100],{"className":17261},[291],") is a vector containing the partial derivatives of the loss function with respect to each parameter, indicating the direction of greatest increase in the loss.",[744,17264,17265,17266,17269],{},"The optimization process continues until a ",[747,17267,17268],{},"convergence"," criterion is met, such as a maximum number of iterations or a minimum improvement in the loss function.",[11,17271,17272],{},"Graphically:",[11,17274,17275,17279],{},[2432,17276],{"alt":17277,"src":17278},"Graphical representation of the gradient descent optimization process","\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations\u002Fshared\u002Fgradient-descent.webp",[2437,17280,17281],{},"Optimization with Gradient Descent",[996,17283,17284,17287,17290,17293],{},[744,17285,17286],{},"The process starts with a random point on the loss function (INITIAL POINT).",[744,17288,17289],{},"The gradient is calculated at that point, indicating the direction of greatest increase in the loss.",[744,17291,17292],{},"The model parameters are updated in the opposite direction of the gradient, with a step size controlled by the learning rate (LEARNING RATE \u002F STEP SIZE).",[744,17294,17295],{},"This process is repeated iteratively until a local or global minimum of the loss function is reached, indicating that the model has been optimized.",[11,17297,17298],{},"The learning rate is crucial for the success of the optimization process:",[741,17300,17301,17311,17320],{},[744,17302,17303,17306,17307,17310],{},[747,17304,17305],{},"DIVERGENCE",": If the learning rate is too ",[747,17308,17309],{},"high",", the model may diverge, jumping over the minimum and increasing the loss.",[744,17312,17313,17306,17316,17319],{},[747,17314,17315],{},"SLOW CONVERGENCE",[747,17317,17318],{},"low",", the optimization process may be very slow, taking a long time to converge or getting stuck in a local minimum.",[744,17321,17322,17325],{},[747,17323,17324],{},"OPTIMAL CONVERGENCE",": An appropriate learning rate allows the model to converge efficiently towards a global or local minimum, effectively optimizing the loss function.",[31,17327,17329],{"id":17328},"the-machine-learning-process","The Machine Learning Process",[11,17331,17332],{},"We can summarize the ML process in:",[996,17334,17335,17346,17357,17368,17378],{},[744,17336,17337,17340,17341],{},[747,17338,17339],{},"Learning Paradigm",": Choose the type of learning (supervised, unsupervised, reinforcement) based on the problem to solve.\n",[741,17342,17343],{},[744,17344,17345],{},"Define the type of problem and available data.",[744,17347,17348,17351,17352],{},[747,17349,17350],{},"Mathematical Model",": Select an appropriate model (regression, classification, clustering) and understand its mathematical formulation.\n",[741,17353,17354],{},[744,17355,17356],{},"Establish the mathematical relationship between input and output.",[744,17358,17359,17362,17363],{},[747,17360,17361],{},"Loss\u002FCost Function",": Define a loss function that measures the model's error and can be optimized.\n",[741,17364,17365],{},[744,17366,17367],{},"Quantifies how bad the model is in its predictions.",[744,17369,17370,17372,17373],{},[747,17371,16930],{},": Use techniques like gradient descent to adjust the model parameters and minimize the loss function.\n",[741,17374,17375],{},[744,17376,17377],{},"Finds the best parameters for the model to make good predictions.",[744,17379,17380,17383,17384],{},[747,17381,17382],{},"Evaluation",": Measure the model's performance using appropriate metrics for the problem type (MSE for regression, accuracy\u002Frecall for classification, etc.).\n",[741,17385,17386],{},[744,17387,17388],{},"Validates the performance of the model and its ability to generalize to unseen data.",{"title":17390,"searchDepth":11114,"depth":11114,"links":17391},"",[17392,17397,17401,17405],{"id":33,"depth":10446,"text":34,"children":17393},[17394,17395,17396],{"id":41,"depth":11114,"text":42},{"id":761,"depth":11114,"text":762},{"id":990,"depth":11114,"text":991},{"id":1029,"depth":10446,"text":1030,"children":17398},[17399,17400],{"id":1043,"depth":11114,"text":755},{"id":11468,"depth":11114,"text":749},{"id":16860,"depth":10446,"text":16861,"children":17402},[17403,17404],{"id":16864,"depth":11114,"text":16865},{"id":16929,"depth":11114,"text":16930},{"id":17328,"depth":10446,"text":17329},"2026-04-06","md","\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations\u002Fshared\u002Fml-paradigms.webp",{},true,"\u002Fblog\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations",{"title":5,"description":13},{"loc":17414,"priority":17415,"lastmod":17406},"\u002Fes\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations",0.7,"machine-learning-paradigms-and-mathematical-foundations","blog\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations","Types of machine learning, common algorithms and essential mathematical foundations for understanding how machine learning models work.",[17420,17421,17422,17423,17420],"Machine Learning","Artificial Intelligence","Python","Data Science","UebnJOgy7GPnhDI7uhV4sfkeLEvIHGR5vGNjmPhJxHE",{"prev":17426,"next":18073},{"id":17427,"title":23,"author":6,"body":17428,"date":18061,"description":17432,"extension":17407,"image":18062,"lastmod":18061,"meta":18063,"navigation":17410,"order":10446,"path":18064,"seo":18065,"sitemap":18066,"slug":18068,"stem":18069,"summary":18070,"tags":18071,"__hash__":18072},"content_en\u002Fblog\u002Fblog\u002Fmachine-learning-fundamentals.md",{"type":8,"value":17429,"toc":18052},[17430,17433,17435,17437,17441,17458,17461,17485,17488,17502,17504,17508,17511,17578,17580,17584,17587,17698,17700,17803,17805,17849,17870,17872,17876,17879,17933,17937,17940,17949,17952,17963,17976,17978,17982,18007,18018,18020,18024,18027,18047,18049],[11,17431,17432],{},"This is the first part of a series of articles where we will explore machine learning, from its basic concepts to neural networks and the creation of a machine learning model. In this first part, we will focus on the fundamentals of machine learning, including what it is, its types and some common algorithms.",[25,17434],{},[28,17436],{},[31,17438,17440],{"id":17439},"artificial-intelligence-and-machine-learning","Artificial Intelligence and Machine Learning",[11,17442,17443,17444,17447,17448,4718,17451,17454,17455,127],{},"You wake up one day, open Netflix and find exactly the series you wanted to watch. Then, you open Google and type something, and the search engine completes your sentence before you finish typing. How do they do this? The answer is: Artificial Intelligence. AI is a discipline focused on developing systems capable of performing tasks that normally require human intelligence, such as ",[747,17445,17446],{},"learning",", ",[747,17449,17450],{},"reasoning",[747,17452,17453],{},"perceiving"," our environment, as well as ",[747,17456,17457],{},"making decisions",[11,17459,17460],{},"There are three types of AI:",[741,17462,17463,17469,17475],{},[744,17464,17465,17468],{},[747,17466,17467],{},"Weak or Narrow AI (Artificial Narrow Intelligence or ANI)",": This is the AI we have today. It is designed to perform specific tasks, such as voice recognition, product recommendations, or automatic translation. It does not have consciousness or real understanding; it simply follows predefined algorithms and patterns. Large Language Models like GPT, Claude, or Gemini are examples of weak AI, as they are designed to process and generate text, \"only\" being statistical models that predict text, and not having a deep understanding of the world or being able to perform tasks outside their specific domain. Although they may seem intelligent, in reality, they are only mimicking language patterns based on the data with which they were trained.",[744,17470,17471,17474],{},[747,17472,17473],{},"General AI (Artificial General Intelligence or AGI)",": This is a hypothetical AI that would have the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. It does not exist yet, but it is a long-term goal in the field of AI.",[744,17476,17477,17480,17481,17484],{},[747,17478,17479],{},"Superintelligence (Artificial Superintelligence or ASI)",": This is an AI that would surpass human intelligence in all aspects, including creativity, problem-solving, and decision-making. It is a ",[747,17482,17483],{},"theoretical"," concept that raises many ethical and philosophical questions about the future of humanity.",[11,17486,17487],{},"Where does Machine Learning fit in? It's very common to confuse AI with Machine Learning, but they aren't exactly the same. We can say that Artificial Intelligence is a broad conceptual umbrella, and under that umbrella lies Machine Learning, a specific subfield that allows computers to learn automatically from data, without the need for a human to program them step by step.",[4606,17489,17490,17493],{},[11,17491,17492],{},"Recommended resources:",[741,17494,17495],{},[744,17496,17497],{},[18,17498,17501],{"href":17499,"rel":17500},"https:\u002F\u002Fyoutu.be\u002FdKqwnCKrpVI?si=g6qqFa_1G_M5P3LS",[22],"Artificial Intelligence vs Machine Learning vs Deep Learning | Machine Learning 101",[28,17503],{},[31,17505,17507],{"id":17506},"ai-subfields","AI Subfields",[11,17509,17510],{},"Within the broad umbrella of AI, there are several subfields that specialize in different aspects of artificial intelligence:",[741,17512,17513,17518,17524,17530,17536,17542,17548,17554,17560,17566,17572],{},[744,17514,17515,17517],{},[747,17516,17420],{},": Focuses on developing algorithms that allow machines to learn from data and improve their performance over time without being explicitly programmed for each specific task.",[744,17519,17520,17523],{},[747,17521,17522],{},"Deep Learning",": Is a branch of machine learning that uses deep neural networks to model and solve complex problems. It is especially effective in tasks such as speech recognition, computer vision, and natural language processing.",[744,17525,17526,17529],{},[747,17527,17528],{},"Natural Language Processing (NLP)",": Focuses on the interaction between computers and human language, allowing machines to understand, interpret, and generate text naturally. This is what makes it possible for chatbots like ChatGPT to maintain coherent conversations with users.",[744,17531,17532,17535],{},[747,17533,17534],{},"Computer Vision",": This field enables machines to understand and process images and videos. It is fundamental for applications such as facial recognition, autonomous driving, and object detection.",[744,17537,17538,17541],{},[747,17539,17540],{},"Robotics",": This field focuses on the design and construction of robots that can perform physical tasks in the real world, from manufacturing to medical assistance.",[744,17543,17544,17547],{},[747,17545,17546],{},"Expert Systems",": These are programs that mimic the decision-making of a human expert in a specific domain, using rules and logic to solve complex problems.",[744,17549,17550,17553],{},[747,17551,17552],{},"Automatic Reasoning",": This field focuses on inferring logical conclusions from formal rules. It is not the same as machine learning. This field includes symbolic logic, problem-solving, and automated planning.",[744,17555,17556,17559],{},[747,17557,17558],{},"Intelligent Agents",": These are systems that can perceive their environment, reason about it, and make decisions to achieve specific goals. They can be as simple as a chatbot or as complex as an autonomous driving system (they can use machine learning, simple rules, logical reasoning, etc.).",[744,17561,17562,17565],{},[747,17563,17564],{},"Distributed AI",": This refers to AI systems that operate across multiple devices or nodes, collaborating to solve problems more efficiently. This is especially relevant in applications such as the Internet of Things (IoT) and cloud computing.",[744,17567,17568,17571],{},[747,17569,17570],{},"Explainable AI (XAI)",": This focuses on developing AI models that are transparent and understandable to humans, allowing users to understand how and why the AI ​​makes certain decisions.",[744,17573,17574,17577],{},[747,17575,17576],{},"AI Ethics and Governance",": This deals with the ethical, legal, and social implications of AI development and use, addressing issues such as privacy, fairness, and transparency.",[28,17579],{},[31,17581,17583],{"id":17582},"how-a-machine-learns","How a Machine Learns",[11,17585,17586],{},"Everything depends on the data we give it:",[741,17588,17589],{},[744,17590,17591,17593,17594,17597,17598,17601,17602,17605,17606,17654,17656,17657,17659,17660,17672,17674,17675,17680,17681,17683,17684],{},[747,17592,42],{},": Here we give the machine clear examples with correct answers already \"labeled\". For example, if we want the AI to help with a medical diagnosis, we give it thousands of medical histories where we already know which patient was ",[747,17595,17596],{},"sick"," and which patient was ",[747,17599,17600],{},"healthy",". The machine learns to recognize patterns in this data to be able to predict the diagnosis of new patients based on what it has learned.",[17603,17604],"br",{},"A simple example would be something like this:",[5935,17607,17608,17624],{},[5938,17609,17610],{},[5941,17611,17612,17615,17618,17621],{},[5944,17613,17614],{},"Age",[5944,17616,17617],{},"Symptoms",[5944,17619,17620],{},"Test Results",[5944,17622,17623],{},"Diagnosis",[5951,17625,17626,17640],{},[5941,17627,17628,17631,17634,17637],{},[5956,17629,17630],{},"45",[5956,17632,17633],{},"Fever, Cough",[5956,17635,17636],{},"Positive",[5956,17638,17639],{},"Sick",[5941,17641,17642,17645,17648,17651],{},[5956,17643,17644],{},"30",[5956,17646,17647],{},"Headache",[5956,17649,17650],{},"Negative",[5956,17652,17653],{},"Healthy",[17603,17655],{},"The label here is the \"Diagnosis\", and the machine learns to associate the features (Age, Symptoms, Test Results) with that label to make future predictions.",[17603,17658],{},"Within supervised learning, there are two main types of tasks:",[741,17661,17662,17667],{},[744,17663,17664,17666],{},[747,17665,749],{},": Where the machine assigns a label to each example. As in classifying emails as \"spam\" or \"not spam\".",[744,17668,17669,17671],{},[747,17670,755],{},": Where the machine predicts a continuous value. For example, predicting the price of a house based on features like size, location, and number of rooms.",[17603,17673],{},"Basically, if the response we want to predict is a category (or a ",[18,17676,17679],{"href":17677,"target":11459,"rel":17678,"ariaLabel":17679},"https:\u002F\u002Fwww.geeksforgeeks.org\u002Fmaths\u002Fdifference-between-discrete-and-continuous-variable\u002F",[11461,11462],"discrete variable","), it's classification, if the response represents a measurable amount on a continuous scale, it's regression.\nAnd the requirement for supervised learning to work well is to have a large and representative dataset, with accurate labels. If the data is scarce or the labels are incorrect, the machine will not be able to learn correctly and its predictions will be inaccurate.",[17603,17682],{},"Examples of ML applications are:",[741,17685,17686,17689,17692,17695],{},[744,17687,17688],{},"Fraud detection in financial transactions (classification)",[744,17690,17691],{},"Stock price prediction (regression)",[744,17693,17694],{},"Image recognition (classification)",[744,17696,17697],{},"Sentiment analysis in social media (classification)",[28,17699],{},[741,17701,17702],{},[744,17703,17704,17706,17707,17709,17710,17748,17750,17751,17800,17802],{},[747,17705,762],{},": Imagine being dropped in an unfamiliar country and having to deduce how society works simply by observing; it's similar. Here, the machine receives unlabeled data and must find hidden patterns on its own. The system will have to analyze similarities, differences, and behaviors to find unusual groupings or patterns. There's no \"teacher\" to tell it if it's right or wrong.",[17603,17708],{},"Main techniques:",[996,17711,17712,17729,17742],{},[744,17713,17714,17717,17718],{},[747,17715,17716],{},"Clustering (Grouping)",": Groups similar data points together.\nIt's typically used for customer segmentation, grouping documents by topic, and automatic image organization, among others. Some algorithms:\n",[741,17719,17720,17723,17726],{},[744,17721,17722],{},"K-means clustering",[744,17724,17725],{},"DBSCAN",[744,17727,17728],{},"Hierarchical clustering",[744,17730,17731,17733,17734],{},[747,17732,984],{},": This seeks to reduce the number of variables while maintaining important information. Used for visualizing complex data and preparing data for other models.\n",[741,17735,17736,17739],{},[744,17737,17738],{},"Principal Component Analysis (PCA)",[744,17740,17741],{},"t-SNE",[744,17743,17744,17747],{},[747,17745,17746],{},"Anomaly Detection",": Identifies data points that behave differently from the rest. Useful for detecting fraud, system failures, and suspicious behaviors.",[17603,17749],{},"A simple example would be:",[5935,17752,17753,17765],{},[5938,17754,17755],{},[5941,17756,17757,17759,17762],{},[5944,17758,17614],{},[5944,17760,17761],{},"Annual Income",[5944,17763,17764],{},"Monthly Expenses",[5951,17766,17767,17778,17789],{},[5941,17768,17769,17772,17775],{},[5956,17770,17771],{},"25",[5956,17773,17774],{},"$30,000",[5956,17776,17777],{},"$1,000",[5941,17779,17780,17783,17786],{},[5956,17781,17782],{},"40",[5956,17784,17785],{},"$80,000",[5956,17787,17788],{},"$3,000",[5941,17790,17791,17794,17797],{},[5956,17792,17793],{},"60",[5956,17795,17796],{},"$50,000",[5956,17798,17799],{},"$2,000",[17603,17801],{},"The machine could group customers into segments based on their income and expenses, without explicitly telling it what groups exist.\nWith this, we can identify consumption patterns, such as young customers tending to spend less than middle-aged customers, or there being a group of customers with high income but low expenses, which could indicate a savings segment.",[28,17804],{},[741,17806,17807],{},[744,17808,17809,17811,17812,127,17817,17819,17820,17842,17844,17845],{},[747,17810,991],{},": Think about how you train a pet with treats. The machine (the agent) takes decisions in an environment and receives \"rewards\" or \"penalties\". This is how Tesla's autonomous driving systems or robots learn to navigate the physical world. A example I like is a video where ",[18,17813,17816],{"href":17814,"target":11459,"rel":17815},"https:\u002F\u002Fyoutu.be\u002FPKDMGPf-PEA?si=tAEMO3cETdPrvi_t",[11461,11462],"they train an agent to play Geometry Dash",[17603,17818],{},"In this type of learning, there are four main components:",[996,17821,17822,17827,17832,17837],{},[744,17823,17824,17826],{},[747,17825,1002],{},": It is the system that takes decisions and learns through interaction with the environment. It can be a robot, a computer program, or any system that can perceive its environment and act upon it.",[744,17828,17829,17831],{},[747,17830,1008],{},": It is the world in which the agent operates. It can be a physical environment, such as a robot in a room, or a virtual environment, such as a video game.",[744,17833,17834,17836],{},[747,17835,1014],{},": It is the signal that the agent receives after taking an action. It can be positive (reward) or negative (penalty) and serves to guide the agent's learning.",[744,17838,17839,17841],{},[747,17840,1020],{},": It is the strategy that the agent uses to decide what action to take based on its current state and the rewards it has received in the past.",[17603,17843],{},"Basically they follow a flow like the following:",[17846,17847],"mermaid-diagram",{"content":17848},"graph TD \nA[Agent] -->|Take action| B(Environment)\nB -->|Provide reward| C[Reward]\nC -->|Update policy| A",[4606,17850,17851,17854],{},[11,17852,17853],{},"Recommended Resources:",[741,17855,17856,17863],{},[744,17857,17858],{},[18,17859,17862],{"href":17860,"rel":17861},"https:\u002F\u002Fyoutu.be\u002FoT3arRRB2Cw?si=ykU9KQjQLxdn9ggj",[22],"What is Supervised and Unsupervised Learning? | DotCSV",[744,17864,17865],{},[18,17866,17869],{"href":17867,"rel":17868},"https:\u002F\u002Fyoutu.be\u002FqBtB-xcJp4c?si=c2GuJBCFPorKGN44",[22],"Reinforcement Learning: The Definitive Guide",[28,17871],{},[31,17873,17875],{"id":17874},"ai-project-pipeline","AI Project Pipeline",[11,17877,17878],{},"An AI project typically follows a structured process that includes several key stages:",[996,17880,17881,17887,17893,17899,17905,17911,17917,17927],{},[744,17882,17883,17886],{},[747,17884,17885],{},"Problem Definition",": It is essential to clearly understand the problem to be solved and the project objectives. This includes identifying the questions to be answered, the expected results, and the success metrics.",[744,17888,17889,17892],{},[747,17890,17891],{},"Data Collection",": The data needed to train the AI ​​model is collected. This can include structured data (such as databases) or unstructured data (such as text, images, or videos). It is important to ensure that the data is high-quality and representative of the problem to be solved.",[744,17894,17895,17898],{},[747,17896,17897],{},"Data Preprocessing",": The collected data often needs to be cleaned and transformed before being used to train the model. This may include removing missing values, normalizing data, coding categorical variables, and splitting the data into training and test sets.",[744,17900,17901,17904],{},[747,17902,17903],{},"Model Selection",": The most suitable machine learning algorithm is chosen for the problem at hand. This may depend on the nature of the data, the complexity of the problem, and the available resources.",[744,17906,17907,17910],{},[747,17908,17909],{},"Model Training",": The training dataset is used to train the AI ​​model. During this stage, the model learns from the data and adjusts its parameters to minimize prediction errors.",[744,17912,17913,17916],{},[747,17914,17915],{},"Model Evaluation",": The model's performance is evaluated using the test set. Specific metrics are used to measure the model's precision, accuracy, sensitivity, and other characteristics, depending on the type of problem (classification, regression, etc.).",[744,17918,17919,17922,17923,17926],{},[747,17920,17921],{},"Hyperparameter Tuning",": If the model's performance is unsatisfactory, the model's hyperparameters can be tuned to improve its performance. This may include changing the model's architecture, adjusting the learning rate, or modifying other algorithm-specific parameters. 8. ",[747,17924,17925],{},"Implementation",": Once the model has been trained and evaluated, it is deployed in a production environment where it can be used to make real-time predictions or process new data.",[744,17928,17929,17932],{},[747,17930,17931],{},"Maintenance and Updating",": After deployment, it is important to monitor the model's performance and update it regularly to ensure it remains effective as data and environmental conditions change.",[31,17934,17936],{"id":17935},"memorizing-vs-learning","Memorizing vs. Learning",[11,17938,17939],{},"When we talk about learning, whether human or machine, there's a crucial concept we must understand: memorizing is not the same as learning.",[11,17941,17942,17945,17946,17948],{},[747,17943,17944],{},"Memorizing"," is like copying and pasting information without truly understanding it. For example, if you memorize the formula for the area of ​​a circle (A = πr²) without understanding what each part means, you won't be able to apply it correctly in different contexts. Memory saves you in the short term, but it doesn't give you the ability to adapt to new situations or solve problems you haven't encountered before. In contrast, ",[747,17947,17446],{}," involves understanding the underlying concepts and being able to apply them to new situations.",[11,17950,17951],{},"In the world of machine learning, a machine that only memorizes training data might perform excellently on that specific data, but then fail spectacularly when used in real-world environments. Therefore, in machine learning, the true goal isn't to memorize specific patterns, but to generalize: to learn rules and relationships that work beyond the examples seen.",[11,17953,17954,17955,17958,17959,17962],{},"But learning isn't easy either. Sometimes, machines suffer from ",[747,17956,17957],{},"overfitting",", which occurs when a model perfectly \"memorizes\" the training data but fails miserably when faced with new, real-world data. It's exactly like a student who memorizes exam answers without truly understanding the concepts. Conversely, if the model is too simple and learns nothing, it suffers from ",[747,17960,17961],{},"underfitting",", like a student who didn't study enough.",[4606,17964,17965,17967],{},[11,17966,17853],{},[741,17968,17969],{},[744,17970,17971],{},[18,17972,17975],{"href":17973,"rel":17974},"https:\u002F\u002Fyoutu.be\u002Fo3DztvnfAJg?si=lorMlPZqLAMa-EV3",[22],"Underfitting and Overfitting: Explained",[28,17977],{},[31,17979,17981],{"id":17980},"the-components-of-an-ai-system","The Components of an AI System",[741,17983,17984,17995,18001],{},[744,17985,17986,17989,17990,127],{},[747,17987,17988],{},"Data",": This is the foundation of any AI system. Without data, there is no learning. It must be high-quality, relevant, and representative of the problem to be solved. It should be high-volume and have the least possible ",[18,17991,17994],{"href":17992,"target":11459,"rel":17993},"https:\u002F\u002Fwww.innovatiana.com\u002Fen\u002Fpost\u002Fbias-estimation-in-machine-learning",[11461,11462],"bias",[744,17996,17997,18000],{},[747,17998,17999],{},"Algorithms",": These are the recipes the machine follows to learn from the data. There are many types of algorithms, each with its own strengths and weaknesses, and they must be carefully selected according to the specific problem to be solved, configured, and fine-tuned to achieve the best possible performance.",[744,18002,18003,18006],{},[747,18004,18005],{},"Infrastructure",": This is the hardware and software necessary to process the data and run the algorithms. This includes everything from servers (CPUs, GPUs, TPUs) to cloud computing platforms and development tools (AWS, Azure, GCP), as well as data storage and database management systems.",[11,18008,18009,18010,18013,18014,18017],{},"In addition to these components, we have ",[747,18011,18012],{},"evaluation",", which is the process of measuring the AI ​​model's performance to ensure it is functioning correctly and meeting the established objectives, as well as the role of ",[747,18015,18016],{},"ethics and governance",", which is crucial for considering the ethical and social implications.",[28,18019],{},[31,18021,18023],{"id":18022},"ethics-in-ai","Ethics in AI",[11,18025,18026],{},"As artificial intelligence becomes more ubiquitous in our lives, it is crucial to consider the ethical implications of its use. Machine learning models can perpetuate existing biases in the data, which can lead to unfair or discriminatory decisions. For example, if a hiring model is trained on historical data that reflects gender or racial bias, the model is likely to reproduce those biases in its recommendations. Furthermore, data privacy is a major concern. It is essential to ensure that the data used is collected and handled ethically, respecting people's privacy and rights.",[741,18028,18029,18035,18041],{},[744,18030,18031,18034],{},[747,18032,18033],{},"Transparency",": Systems must be understandable and auditable so that users can understand how they work and why they make certain decisions. One solution to this is explainable AI (XAI).",[744,18036,18037,18040],{},[747,18038,18039],{},"Explainability",": AI models must be able to explain their decisions clearly and comprehensibly to users, which helps build trust and allows users to understand the reasons behind the system's recommendations or actions.",[744,18042,18043,18046],{},[747,18044,18045],{},"Accountability",": It must be clearly established who is responsible for the decisions made by AI systems, especially in cases where those decisions can have a significant impact on people's lives. This is where legal frameworks and regulations come into play, which must be developed to ensure that AI companies and developers are held accountable for their creations.",[28,18048],{},[11,18050,18051],{},"This concludes the first part of this series of articles on machine learning. In the next part, we will explore some machine learning paradigms and certain mathematical foundations that are essential for understanding how the algorithms work.",{"title":17390,"searchDepth":11114,"depth":11114,"links":18053},[18054,18055,18056,18057,18058,18059,18060],{"id":17439,"depth":10446,"text":17440},{"id":17506,"depth":10446,"text":17507},{"id":17582,"depth":10446,"text":17583},{"id":17874,"depth":10446,"text":17875},{"id":17935,"depth":10446,"text":17936},{"id":17980,"depth":10446,"text":17981},{"id":18022,"depth":10446,"text":18023},"2026-03-30","\u002Fblog\u002Fmachine-learning-fundamentals\u002Fshared\u002Fml-fundamentals.webp",{},"\u002Fblog\u002Fblog\u002Fmachine-learning-fundamentals",{"title":23,"description":17432},{"loc":18067,"priority":17415,"lastmod":18061},"\u002Fes\u002Fblog\u002Fmachine-learning-fundamentals","machine-learning-fundamentals","blog\u002Fblog\u002Fmachine-learning-fundamentals","Basic concepts for starting in the world of machine learning",[17420,17421,17422,17423,17420],"pEcYe634ioebWgtBZVCgWgEWVuXQtl0dUEymnA919Tg",{"id":18074,"title":18075,"author":6,"body":18076,"date":19857,"description":18080,"extension":17407,"image":19858,"lastmod":19857,"meta":19859,"navigation":17410,"order":19860,"path":19861,"seo":19862,"sitemap":19863,"slug":19865,"stem":19866,"summary":19867,"tags":19868,"__hash__":19871},"content_en\u002Fblog\u002Fblog\u002Fworkflow-machine-learning-projects.md","The workflow in Machine Learning projects",{"type":8,"value":18077,"toc":19820},[18078,18081,18088,18090,18092,18095,18121,18135,18144,18146,18150,18153,18178,18181,18184,18206,18208,18212,18215,18241,18244,18248,18251,18254,18258,18261,18316,18319,18344,18349,18353,18379,18383,18386,18412,18415,18419,18476,18480,18488,18491,18500,18508,18514,18522,18528,18536,18540,18543,18569,18577,18583,18586,18590,18593,18631,18634,18638,18641,18658,18660,18666,18670,18695,18699,18702,18704,18718,18722,18725,18742,18745,18749,18752,18755,18758,18778,18783,18786,18824,18828,18831,18872,18876,18879,18921,18925,18928,18932,18935,18939,18942,18974,18978,18981,19001,19004,19042,19045,19077,19080,19106,19108,19112,19115,19120,19123,19148,19151,19157,19160,19166,19173,19179,19186,19189,19195,19198,19204,19209,19215,19222,19225,19232,19235,19241,19244,19247,19250,19257,19263,19270,19276,19280,19283,19289,19292,19298,19304,19308,19311,19318,19321,19327,19334,19337,19343,19350,19353,19359,19362,19366,19369,19412,19415,19421,19428,19431,19434,19445,19448,19507,19513,19524,19530,19532,19535,19546,19549,19552,19555,19558,19566,19569,19610,19613,19616,19619,19660,19663,19714,19717,19720,19723,19737,19740,19744,19747,19750,19753,19761,19767,19770,19773,19781,19783,19790,19801,19804,19811,19818],[11,18079,18080],{},"We continue learning about the world of machine learning, and this time, we will delve into the typical flow of a machine learning project.",[11,18082,16,18083],{},[18,18084,18087],{"href":18085,"rel":18086},"https:\u002F\u002Fderas.dev\u002Fblog\u002Fmachine-learning-paradigms-and-mathematical-foundations",[22],"Paradigms of machine learning and mathematical foundations",[25,18089],{},[28,18091],{},[11,18093,18094],{},"Why is it important to understand the workflow of a machine learning project?",[741,18096,18097,18103,18109,18115],{},[744,18098,18099,18102],{},[747,18100,18101],{},"The process is more important than the result",": Successful models do not depend solely on sophisticated algorithms, but on a well-structured process. A Random Forest with clean and well-prepared data consistently outperforms a deep neural network with poor-quality data.",[744,18104,18105,18108],{},[747,18106,18107],{},"Reproducibility",": A clear and documented workflow allows other data scientists to reproduce your results, which is fundamental for validation and advancing knowledge in the field.",[744,18110,18111,18114],{},[747,18112,18113],{},"Collaboration",": In machine learning projects, there are often multiple people involved, from data scientists to data engineers and stakeholders. A well-defined workflow facilitates communication and collaboration among all team members.",[744,18116,18117,18120],{},[747,18118,18119],{},"Reduces the risk of errors",": A structured process helps identify and correct errors in the early stages of the project, which can save time and resources in the long run.",[11,18122,18123,18124,18127,18128,4718,18131,18134],{},"In general, machine learning is a process that is ",[747,18125,18126],{},"part of a system",", with an ",[747,18129,18130],{},"iterative cycle",[747,18132,18133],{},"value-driven approach"," that consists of several stages, each with their own tasks and challenges.",[11,18136,18137,18141],{},[2432,18138],{"alt":18139,"src":18140},"Integrated View of the Machine Learning Cycle","\u002Fblog\u002Fworkflow-machine-learning-projects\u002Fshared\u002Fmachine-learning-cycle.webp",[2437,18142,18143],{},"Machine Learning Cycle",[28,18145],{},[31,18147,18149],{"id":18148},"_1-problem-definition","1. Problem Definition",[11,18151,18152],{},"The first stage of any machine learning project is the problem definition:",[741,18154,18155,18161,18167,18173],{},[744,18156,18157,18160],{},[747,18158,18159],{},"Problem Identification",": What do we want to achieve with the project? What decisions do we want to support with the model? It is crucial to understand the business context or application to clearly define the problem.",[744,18162,18163,18166],{},[747,18164,18165],{},"Target Variable",": What is the variable we want to predict or classify? This variable, also known as the dependent variable, is the focus of the project and must be clearly defined.",[744,18168,18169,18172],{},[747,18170,18171],{},"Type of Problem",": Is it a classification, regression, clustering, or something else? The nature of the problem will influence the choice of algorithms and techniques to use.",[744,18174,18175,18177],{},[747,18176,16865],{},": How will we measure the success of the model? It is important to define evaluation metrics from the beginning, as these will guide the model development and decision-making throughout the project.",[11,18179,18180],{},"This stage is the foundation of the entire project. A poorly defined problem can lead to wasted efforts and unsatisfactory results. It is fundamental to dedicate time to understanding the problem and setting clear objectives before moving on to subsequent stages.",[11,18182,18183],{},"Let's look at a practical example:\nSuppose an e-commerce company wants to predict whether a customer will make a purchase on their website. In this case:",[741,18185,18186,18191,18196,18201],{},[744,18187,18188,18190],{},[747,18189,18159],{},": Predicting the probability of a customer making a purchase.",[744,18192,18193,18195],{},[747,18194,18165],{},": The target variable could be a binary variable indicating whether the customer made a purchase (1) or not (0).",[744,18197,18198,18200],{},[747,18199,18171],{},": This is a binary classification problem.",[744,18202,18203,18205],{},[747,18204,16865],{},": The evaluation metrics could include accuracy, recall, and F1-score, depending on the relative importance of false positives and false negatives in the business context.",[28,18207],{},[31,18209,18211],{"id":18210},"_2-data-collection","2. Data Collection",[11,18213,18214],{},"Once the problem is clearly defined, the next step is data collection, where relevant data sources must be identified and accessed. This can include:",[741,18216,18217,18223,18229,18235],{},[744,18218,18219,18222],{},[747,18220,18221],{},"Internal\u002FEnterprise Databases",": Data stored in internal company systems, such as relational databases, data warehouses or data lakes. Some examples include sales records, customer data, data from CRM or ERP systems, among others.",[744,18224,18225,18228],{},[747,18226,18227],{},"APIs and Web Services",": External data from data providers, social media, geolocation services, etc. For example, a sentiment analysis company could use the Twitter API to collect tweets related to a specific topic.",[744,18230,18231,18234],{},[747,18232,18233],{},"System Logs and Event Records",": Data generated by applications, servers, IoT devices, etc. For example, an infrastructure monitoring company could collect server logs to detect failure patterns.",[744,18236,18237,18240],{},[747,18238,18239],{},"Public\u002FExternal Data",": Data available publicly, such as datasets from Kaggle, government data, academic research data, etc. For example, a researcher could use the MNIST image dataset to train a handwritten digit recognition model.",[11,18242,18243],{},"It is important to note that the quality of the collected data is crucial for the success of the project. The data must be relevant, complete, accurate and up-to-date. Additionally, it is fundamental to consider ethical and legal aspects related to data collection and usage, such as user privacy and compliance with regulations like GDPR.",[31,18245,18247],{"id":18246},"_3-data-preprocessing","3. Data Preprocessing",[11,18249,18250],{},"This is the phase of preparation and cleaning of data, which is a crucial step and often the biggest bottleneck. Data professionals usually dedicate between 70% and 80% of their time to preparing data and not to building models.",[11,18252,18253],{},"The quality of the models depends directly on the quality of the data; if the data is disorganized or incomplete, the model will not be able to learn useful patterns. Even the most sophisticated algorithm cannot compensate for poor-quality data.",[31,18255,18257],{"id":18256},"_4-exploratory-data-analysis-eda","4. Exploratory Data Analysis (EDA)",[11,18259,18260],{},"During this stage, a detailed analysis of the data is performed to understand its structure, distribution, and relationships between variables. This includes:",[741,18262,18263,18283,18300],{},[744,18264,18265,18268,18269],{},[747,18266,18267],{},"Univariate Analysis",": Examining the distribution of each variable individually, using descriptive statistics and visualizations such as:\n",[741,18270,18271,18274,18277,18280],{},[744,18272,18273],{},"Histograms",[744,18275,18276],{},"Boxplots",[744,18278,18279],{},"Bar Charts",[744,18281,18282],{},"Measures of central tendency (mean, median) and dispersion (standard deviation, interquartile range)",[744,18284,18285,18288,18289],{},[747,18286,18287],{},"Bivariate Analysis",": Exploring the relationships between pairs of variables, using visualizations such as:\n",[741,18290,18291,18294,18297],{},[744,18292,18293],{},"Scatter Plots",[744,18295,18296],{},"Heatmaps for visualizing correlations",[744,18298,18299],{},"Stacked Bar Charts for categorical variables",[744,18301,18302,18305,18306],{},[747,18303,18304],{},"Multivariate Analysis",": Examining the relationships between multiple variables simultaneously, utilizing techniques such as:\n",[741,18307,18308,18310,18313],{},[744,18309,17738],{},[744,18311,18312],{},"Cluster Analysis",[744,18314,18315],{},"Pair Plots\nThe EDA is fundamental for detecting problems in the data, such as outliers, skewed distributions or non-linear relationships between variables. Additionally, the EDA can provide valuable insights that will guide the feature selection and algorithm choice in the subsequent stages of the project.",[11,18317,18318],{},"The common tools for performing EDA include:",[741,18320,18321,18326,18332,18338],{},[744,18322,18323,18325],{},[747,18324,17422],{},": Bibliotecas como Pandas, Matplotlib, Seaborn y Plotly son ampliamente utilizadas para el análisis exploratorio de datos en Python.",[744,18327,18328,18331],{},[747,18329,18330],{},"R",": Paquetes como ggplot2, dplyr y tidyr son populares para realizar EDA en R.",[744,18333,18334,18337],{},[747,18335,18336],{},"Herramientas de visualización",": Herramientas como Tableau, Power BI o QlikView también pueden ser utilizadas para realizar análisis exploratorio de datos de manera interactiva.",[744,18339,18340,18343],{},[747,18341,18342],{},"Jupyter Notebooks",": Los notebooks de Jupyter son una herramienta común para realizar EDA, ya que permiten combinar código, visualizaciones y texto explicativo en un solo documento.",[4606,18345,18346],{},[11,18347,18348],{},"The EDA is not a linear step, often it is performed iteratively as new insights are discovered or problems in the data are identified. It is important to document the findings of the EDA, as these can be useful for decision-making in the subsequent stages of the project.",[39,18350,18352],{"id":18351},"principles-for-effective-eda","Principles for Effective EDA",[741,18354,18355,18361,18367,18373],{},[744,18356,18357,18360],{},[747,18358,18359],{},"Start Simple",": Begin with basic visualizations and statistics to gain a general understanding of the data before diving into more complex analyses.",[744,18362,18363,18366],{},[747,18364,18365],{},"Purposeful Use of Colors",": Use colors strategically to highlight important patterns or differences in the data, avoiding excessive use of colors that might be distracting.",[744,18368,18369,18372],{},[747,18370,18371],{},"Iterative Process",": Continuously iterate as new insights are discovered or problems in the data are identified, adjusting the EDA approach as needed.",[744,18374,18375,18378],{},[747,18376,18377],{},"Document Findings",": Record the insights and discoveries from the EDA to facilitate decision-making in subsequent stages of the project and to share with other team members.",[31,18380,18382],{"id":18381},"_5-feature-engineering","5. Feature engineering",[11,18384,18385],{},"The feature engineering is the process of creating new features from the original data to improve the model's performance. It is the bridge between raw unstructured data and inputs ready for modeling. This stage is crucial because it helps us:",[741,18387,18388,18394,18400,18406],{},[744,18389,18390,18393],{},[747,18391,18392],{},"Improve Accuracy",": Well-designed features can capture complex patterns in the data that models can leverage to make better predictions.",[744,18395,18396,18399],{},[747,18397,18398],{},"Reduce Overfitting",": By creating more relevant features, we can help models generalize better to unseen data, reducing the risk of overfitting.",[744,18401,18402,18405],{},[747,18403,18404],{},"Facilitate Interpretation",": Well-designed features can make models more interpretable, which is especially important in applications where explainability is crucial.",[744,18407,18408,18411],{},[747,18409,18410],{},"Increase Efficiency",": By reducing the dimensionality of the data or creating more informative features, we can improve the efficiency of model training.",[11,18413,18414],{},"Some fundamental techniques for feature engineering include:",[39,18416,18418],{"id":18417},"numerical-transformations","Numerical Transformations",[741,18420,18421,18441],{},[744,18422,18423,18426,18427],{},[747,18424,18425],{},"Scaling",": Useful for models sensitive to magnitude (Regression, SVM, KNN, neural networks).",[741,18428,18429,18435,18438],{},[744,18430,18431,18432],{},"Min-Max Scaling: Maps values to range ",[50,18433,18434],{},"0,1",[744,18436,18437],{},"Standardization (Z-score): Mean 0, standard deviation 1",[744,18439,18440],{},"Robust Scaling: Uses median and interquartile range (better with outliers)",[744,18442,18443,18446,18447],{},[747,18444,18445],{},"Non-linear Transformations",": When the relationship is not linear.",[741,18448,18449,18456,18462,18473],{},[744,18450,18451,18452],{},"Log transform: ",[18453,18454,18455],"code",{},"log(x)",[744,18457,18458,18459],{},"Square root: ",[18453,18460,18461],{},"sqrt(x)",[744,18463,18464,18465,18468,18469,18472],{},"Box-Cox: ",[18453,18466,18467],{},"((x + 1)^λ - 1) \u002F λ"," (for λ ≠ 0) or ",[18453,18470,18471],{},"log(x + 1)"," (for λ = 0)",[744,18474,18475],{},"Yeo-Johnson: Similar to Box-Cox but for data with negative values\nVery useful when there are highly skewed distributions.",[39,18477,18479],{"id":18478},"categorical-variables","Categorical Variables",[741,18481,18482],{},[744,18483,18484,18487],{},[747,18485,18486],{},"One-Hot Encoding",": Converts categories into binary columns.",[11,18489,18490],{},"Example:",[18492,18493,18498],"pre",{"className":18494,"code":18496,"language":18497},[18495],"language-text","Color: [Red, Blue, Green]\n\nBecome:\n\nRed  Blue  Green\n1     0     0\n","text",[18453,18499,18496],{"__ignoreMap":17390},[741,18501,18502],{},[744,18503,18504,18507],{},[747,18505,18506],{},"Ordinal Encoding",": When there is an order:",[18492,18509,18512],{"className":18510,"code":18511,"language":18497},[18495],"Low \u003C Medium \u003C High\n",[18453,18513,18511],{"__ignoreMap":17390},[741,18515,18516],{},[744,18517,18518,18521],{},[747,18519,18520],{},"Target Encoding",": Replaces category with average of the target:",[18492,18523,18526],{"className":18524,"code":18525,"language":18497},[18495],"City → average sales\n",[18453,18527,18525],{"__ignoreMap":17390},[741,18529,18530],{},[744,18531,18532,18535],{},[747,18533,18534],{},"Frequency Encoding",": Replaces category with its frequency of occurrence.",[39,18537,18539],{"id":18538},"temporal-features","Temporal Features",[11,18541,18542],{},"When working with dates, we can extract features such as:",[741,18544,18545,18548,18551,18554,18557,18560,18563,18566],{},[744,18546,18547],{},"Year",[744,18549,18550],{},"Month",[744,18552,18553],{},"Day",[744,18555,18556],{},"Day of the week",[744,18558,18559],{},"Is weekend",[744,18561,18562],{},"Quarter",[744,18564,18565],{},"Date difference",[744,18567,18568],{},"Time since last event",[741,18570,18571],{},[744,18572,18573,18576],{},[747,18574,18575],{},"Cyclical Encoding",": For variables like hour or month:",[18492,18578,18581],{"className":18579,"code":18580,"language":18497},[18495],"sin(2π * hour \u002F 24)\ncos(2π * hour \u002F 24)\n",[18453,18582,18580],{"__ignoreMap":17390},[11,18584,18585],{},"This prevents 23 and 0 from seeming \"far apart\".",[39,18587,18589],{"id":18588},"interactions-between-variables","Interactions between Variables",[11,18591,18592],{},"Sometimes the combination matters more than the single variable.",[741,18594,18595,18604,18615,18623],{},[744,18596,18597,18600,18601],{},[747,18598,18599],{},"Product of variables",": ",[18453,18602,18603],{},"x1 * x2",[744,18605,18606,18600,18609,17447,18612],{},[747,18607,18608],{},"Polynomials",[18453,18610,18611],{},"x^2",[18453,18613,18614],{},"x^3",[744,18616,18617,18600,18620],{},[747,18618,18619],{},"Ratios",[18453,18621,18622],{},"price \u002F size",[744,18624,18625,18600,18628],{},[747,18626,18627],{},"Differences",[18453,18629,18630],{},"payment_date - registration_date",[11,18632,18633],{},"Very useful in linear models.",[39,18635,18637],{"id":18636},"binning-discretization","Binning (Discretization)",[11,18639,18640],{},"Convert numbers into categories:",[741,18642,18643,18648,18653],{},[744,18644,18645],{},[747,18646,18647],{},"Binning uniforme",[744,18649,18650],{},[747,18651,18652],{},"Binning por cuantiles",[744,18654,18655],{},[747,18656,18657],{},"Binning basado en negocio",[11,18659,18490],{},[18492,18661,18664],{"className":18662,"code":18663,"language":18497},[18495],"Age → [0-18], [19-35], [36-60], 60+\n",[18453,18665,18663],{"__ignoreMap":17390},[39,18667,18669],{"id":18668},"handling-outliers","Handling Outliers",[741,18671,18672,18677,18682,18687],{},[744,18673,18674],{},[747,18675,18676],{},"Clipping",[744,18678,18679],{},[747,18680,18681],{},"Winsorizing",[744,18683,18684],{},[747,18685,18686],{},"Log transform",[744,18688,18689,18600,18692],{},[747,18690,18691],{},"Create binary feature",[18453,18693,18694],{},"es_outlier",[39,18696,18698],{"id":18697},"cluster-based-features","Cluster-Based Features",[11,18700,18701],{},"Very powerful in transactional datasets.",[11,18703,18490],{},[741,18705,18706,18709,18712,18715],{},[744,18707,18708],{},"Average purchases per user",[744,18710,18711],{},"Number of orders",[744,18713,18714],{},"Time since last purchase",[744,18716,18717],{},"Historical maximum\u002Fminimum",[39,18719,18721],{"id":18720},"feature-selection","Feature Selection",[11,18723,18724],{},"It's not all about creating - it's also about deleting.",[741,18726,18727,18730,18733,18736,18739],{},[744,18728,18729],{},"Correlation",[744,18731,18732],{},"Mutual information",[744,18734,18735],{},"RFE",[744,18737,18738],{},"Lasso (L1)",[744,18740,18741],{},"Feature importance (trees)",[11,18743,18744],{},"Feature engineering is one of the most valuable skills in data science, as it can make the difference between a mediocre model and an exceptional one.",[31,18746,18748],{"id":18747},"_6-training-of-models","6. Training of Models",[11,18750,18751],{},"Once the data is prepared and the features are designed, the next step is to train a machine learning model.",[11,18753,18754],{},"In this stage, an appropriate machine learning algorithm is selected for the defined problem and adjusted to the training data. The training process involves feeding the model with data and allowing it to learn patterns and relationships to make predictions.",[11,18756,18757],{},"The first thing we must consider before starting is the division of the data (datasets) into training, validation, and test sets. This is crucial for evaluating the model's performance fairly and avoiding overfitting:",[741,18759,18760,18766,18772],{},[744,18761,18762,18765],{},[747,18763,18764],{},"Training Set (70-80%)",": This is the set of data used to train the model. The model learns from these data, adjusting its parameters to minimize error in predictions.",[744,18767,18768,18771],{},[747,18769,18770],{},"Validation Set (10-15%)",": This is a separate set of data used to tune the model's hyperparameters and make decisions about the model's architecture. The model is not directly trained on this data, but it is used to evaluate its performance during the training process.",[744,18773,18774,18777],{},[747,18775,18776],{},"Test Set (10-15%)",": This is a completely separate set of data used to evaluate the final performance of the model after training and hyperparameter selection. This set is not used at all during the training or validation processes, allowing for an unbiased evaluation of the model.",[4606,18779,18780],{},[11,18781,18782],{},"As a tip, if you use AI agents for software development, a good practice is to use different sessions or agents for each stage of development, one agent for code generation, another for review, and another for testing. In this case, it helps ensure that the AI is not self-referential and can detect errors that a single agent might miss.",[11,18784,18785],{},"For model training, an appropriate machine learning algorithm is selected for the type of problem being addressed (classification, regression, clustering, etc.). Some common examples include:",[741,18787,18788,18794,18800,18806,18812,18818],{},[744,18789,18790,18793],{},[747,18791,18792],{},"Linear Regression",": A simple model for linear relationships, fast and interpretable but limited to linear relationships.",[744,18795,18796,18799],{},[747,18797,18798],{},"Decision Trees",": Models based on rules, easy to interpret but prone to overfitting.",[744,18801,18802,18805],{},[747,18803,18804],{},"Random Forest",": A collection of decision trees that reduces overfitting but is less interpretable.",[744,18807,18808,18811],{},[747,18809,18810],{},"Gradient Boosting (XGBoost, LightGBM)",": Potente para datos tabulares, pero puede ser lento y propenso a sobreajuste si no se ajusta correctamente.",[744,18813,18814,18817],{},[747,18815,18816],{},"Redes neuronales",": Modelos inspirados en el cerebro, capaces de capturar relaciones complejas, pero requieren grandes cantidades de datos y son menos interpretables.",[744,18819,18820,18823],{},[747,18821,18822],{},"Support Vector Machines (SVM)",": Efectivo para problemas de clasificación, pero puede ser lento con grandes conjuntos de datos.",[31,18825,18827],{"id":18826},"_7-model-evaluation","7. Model Evaluation",[11,18829,18830],{},"Once the model has been trained, it is crucial to evaluate its performance using the validation and test sets. Model evaluation involves measuring its ability to make accurate predictions and generalize to unseen data.\nEvaluation metrics vary depending on the type of problem being addressed. For classification problems, some common metrics include:",[741,18832,18833,18838,18843,18849,18855,18860,18866],{},[744,18834,18835,18837],{},[747,18836,16908],{},": The proportion of correct predictions over the total number of predictions made.",[744,18839,18840,18842],{},[747,18841,16914],{},": The proportion of true positives over the total number of actual positives.",[744,18844,18845,18848],{},[747,18846,18847],{},"F1-score",": The harmonic mean of precision and recall, useful when there is an imbalance between classes.",[744,18850,18851,18854],{},[747,18852,18853],{},"AUC-ROC",": Area under the ROC curve, which measures the model's ability to distinguish between classes.\nFor regression problems, some common metrics include:",[744,18856,18857,18859],{},[747,18858,16878],{},": The average of the squares of the errors between predictions and actual values.",[744,18861,18862,18865],{},[747,18863,18864],{},"Mean Absolute Error (MAE)",": The average of the absolute values of the errors between predictions and actual values.",[744,18867,18868,18871],{},[747,18869,18870],{},"R² (Coeficiente de determinación)",": The proportion of the variance in the dependent variable that is predictable from the independent variables.",[39,18873,18875],{"id":18874},"confusion-matrix","Confusion Matrix",[11,18877,18878],{},"A useful tool for evaluating classification models is the confusion matrix, which shows the number of true positives, false positives, true negatives, and false negatives. This allows for a better understanding of the model's performance and the areas where it may be making errors.",[5935,18880,18881,18893],{},[5938,18882,18883],{},[5941,18884,18885,18887,18890],{},[5944,18886],{},[5944,18888,18889],{},"Predicted Positive",[5944,18891,18892],{},"Predicted Negative",[5951,18894,18895,18908],{},[5941,18896,18897,18902,18905],{},[5956,18898,18899],{},[747,18900,18901],{},"Actual Positive",[5956,18903,18904],{},"True Positives (TP)",[5956,18906,18907],{},"False Negatives (FN)",[5941,18909,18910,18915,18918],{},[5956,18911,18912],{},[747,18913,18914],{},"Actual Negative",[5956,18916,18917],{},"False Positives (FP)",[5956,18919,18920],{},"True Negatives (TN)",[39,18922,18924],{"id":18923},"roc-curve","ROC Curve",[11,18926,18927],{},"The ROC curve (Receiver Operating Characteristic) is a graphical tool that shows the relationship between the true positive rate (TPR) and the false positive rate (FPR) as the classification threshold is varied. The area under the ROC curve (AUC-ROC) is a metric that measures the model's ability to distinguish between classes, with a value of 1 indicating a perfect model and a value of 0.5 indicating a model with no discrimination capability.",[39,18929,18931],{"id":18930},"precision-recall-curve","Precision-Recall Curve",[11,18933,18934],{},"The precision-recall curve is another graphical tool that shows the relationship between precision and recall as the classification threshold is varied. This curve is especially useful when there is an imbalance between classes, as it focuses on the model's ability to correctly identify the minority class.",[31,18936,18938],{"id":18937},"_8-implementation-and-deployment","8. Implementation and Deployment",[11,18940,18941],{},"Once the model has been trained and evaluated, the next step is to implement it in a production environment so that it can be used by end users or integrated into existing systems. The implementation and deployment of machine learning models can be challenging due to the need to ensure scalability, security, and maintainability of the model in a production environment. Some key considerations for implementing and deploying machine learning models include:",[741,18943,18944,18950,18956,18962,18968],{},[744,18945,18946,18949],{},[747,18947,18948],{},"APIs",": Expose the model through a RESTful API or gRPC so that it can be consumed by other applications or services.",[744,18951,18952,18955],{},[747,18953,18954],{},"Web Applications",": Integrate the model into a web application so that users can interact with it through a graphical interface.",[744,18957,18958,18961],{},[747,18959,18960],{},"Integration with Existing Systems",": Integrate the model into existing enterprise systems, such as CRM, ERP, or recommendation systems.",[744,18963,18964,18967],{},[747,18965,18966],{},"Containers and Orchestration",": Use containers (Docker) and orchestration tools (Kubernetes) to facilitate deployment, scalability, and management of the model in production.",[744,18969,18970,18973],{},[747,18971,18972],{},"Monitoring and Maintenance",": Implement monitoring systems to track the model's performance in production, detect potential issues, and perform updates or retrainings as needed.",[31,18975,18977],{"id":18976},"_9-monitoring-and-maintenance","9. Monitoring and Maintenance",[11,18979,18980],{},"Once the model is in production, it is crucial to monitor its performance continuously to ensure it remains effective and relevant. Model monitoring involves tracking key metrics, detecting potential issues, and performing adjustments or retrainings as needed. Some issues that may arise during this stage include:",[741,18982,18983,18989,18995],{},[744,18984,18985,18988],{},[747,18986,18987],{},"Data Drift",": Occurs when the distribution of input data changes over time, which can negatively impact the model's performance. It is important to monitor the data distribution and perform retrainings if a significant drift is detected.",[744,18990,18991,18994],{},[747,18992,18993],{},"Concept Drift",": Occurs when the relationship between features and the target variable changes over time, making the model less effective. It is important to monitor the model's performance and perform adjustments or retrainings if concept drift is detected.",[744,18996,18997,19000],{},[747,18998,18999],{},"Training-Serving Skew",": Occurs when there are differences between the data used to train the model and the data found in production, which can negatively impact the model's performance. It is important to ensure that training data is representative of production data and perform adjustments if a significant skew is detected.",[11,19002,19003],{},"To achieve effective monitoring, some best practices can be implemented, such as:",[741,19005,19006,19012,19018,19024,19030,19036],{},[744,19007,19008,19011],{},[747,19009,19010],{},"Defining clear KPIs",": Establish key performance metrics (KPIs) to monitor the model, such as accuracy, recall, F1-score, AUC-ROC, etc.",[744,19013,19014,19017],{},[747,19015,19016],{},"Implementing alerts",": Configure alerts to notify the team when the model's performance drops below a predefined threshold or when significant drift is detected.",[744,19019,19020,19023],{},[747,19021,19022],{},"Diversifying metrics",": Monitor multiple metrics to obtain a comprehensive view of the model's performance and detect potential issues from different angles.",[744,19025,19026,19029],{},[747,19027,19028],{},"Automating retrainings",": Set up automated processes to perform retrainings of the model when significant drift is detected or when the performance falls below a predefined threshold.",[744,19031,19032,19035],{},[747,19033,19034],{},"Documenting changes",": Maintain a record of the changes made to the model, such as hyperparameter adjustments, changes in training data, etc., to facilitate traceability and understanding of the decisions made.",[744,19037,19038,19041],{},[747,19039,19040],{},"Versioning models",": Use versioning tools for models to maintain a history of different versions of the model and facilitate change management and updates.",[11,19043,19044],{},"Typically, the maintenance process follows a procedure like the following:",[996,19046,19047,19053,19059,19065,19071],{},[744,19048,19049,19052],{},[747,19050,19051],{},"Continuous monitoring",": Track the model's performance in production using the defined key metrics.",[744,19054,19055,19058],{},[747,19056,19057],{},"Problem detection",": Identify potential problems, such as data drift, concept drift or training-serving skew, through metric and alert monitoring.",[744,19060,19061,19064],{},[747,19062,19063],{},"Root Cause Analysis",": Investigate the underlying causes of the detected problems, such as changes in data distribution, changes in user behavior, etc.",[744,19066,19067,19070],{},[747,19068,19069],{},"Adjustments or Retraining",": Make adjustments to the model or perform retraining using new data to address the detected problems and improve model performance.",[744,19072,19073,19076],{},[747,19074,19075],{},"Validation and Deployment",": Validate the performance of the adjusted or retrained model using the validation set and then deploy the new version of the model to production.",[11,19078,19079],{},"Some popular tools for monitoring and maintaining machine learning models include:",[741,19081,19082,19088,19094,19100],{},[744,19083,19084,19087],{},[747,19085,19086],{},"Prometheus",": An open-source monitoring and alerting system that can be used to track model performance metrics in production.",[744,19089,19090,19093],{},[747,19091,19092],{},"Grafana",": A data visualization platform that can be integrated with Prometheus to create custom dashboards for monitoring model performance.",[744,19095,19096,19099],{},[747,19097,19098],{},"MLflow",": An open-source platform for managing the lifecycle of machine learning models, including features for monitoring and maintaining models in production.",[744,19101,19102,19105],{},[747,19103,19104],{},"Evidently AI",": Evidently AI is an open-source, cloud-based platform for evaluating, testing, and monitoring AI and machine learning systems.",[28,19107],{},[31,19109,19111],{"id":19110},"case-study-churn-prediction-in-a-fintech-company","Case Study: Churn Prediction in a Fintech Company",[11,19113,19114],{},"We will explore a simulated case study of a machine learning project designed to predict churn in a digital subscription fintech company. This case will illustrate our current understanding of the workflow in a machine learning project.",[11,19116,19117],{},[747,19118,19119],{},"Business Context",[11,19121,19122],{},"A digital subscription fintech company has:",[741,19124,19125,19130,19136,19142],{},[744,19126,19127],{},[747,19128,19129],{},"120,000 active users",[744,19131,19132,19133],{},"Average monthly subscription: ",[747,19134,19135],{},"$25",[744,19137,19138,19139],{},"Monthly Recurring Revenue (MRR): ",[747,19140,19141],{},"$3,000,000",[744,19143,19144,19145],{},"Monthly churn rate: ",[747,19146,19147],{},"8%",[11,19149,19150],{},"This means that each month:",[18492,19152,19155],{"className":19153,"code":19154,"language":18497},[18495],"120,000 × 8% = 9,600 users cancel\n",[18453,19156,19154],{"__ignoreMap":17390},[11,19158,19159],{},"Estimated monthly loss:",[18492,19161,19164],{"className":19162,"code":19163,"language":18497},[18495],"9,600 × $25 = $240,000\n",[18453,19165,19163],{"__ignoreMap":17390},[11,19167,19168,19169,19172],{},"The company wants to reduce churn to ",[747,19170,19171],{},"6%",", which would mean saving:",[18492,19174,19177],{"className":19175,"code":19176,"language":18497},[18495],"2% × 120,000 × $25 = $60,000 per month\n",[18453,19178,19176],{"__ignoreMap":17390},[11,19180,19181,19182,19185],{},"The goal of the Machine Learning project is to ",[747,19183,19184],{},"identify users with a high probability of canceling within the next 30 days",", to send them a personalized retention campaign.",[39,19187,17885],{"id":19188},"problem-definition",[741,19190,19191],{},[744,19192,19193],{},[747,19194,18159],{},[11,19196,19197],{},"Reduce the monthly churn rate from 8% to 6%.",[741,19199,19200],{},[744,19201,19202],{},[747,19203,18165],{},[11,19205,19206,669],{},[18453,19207,19208],{},"churn_30d",[18492,19210,19213],{"className":19211,"code":19212,"language":18497},[18495],"1 - Cancels within the next 30 days\n0 - Does not cancel\n",[18453,19214,19212],{"__ignoreMap":17390},[741,19216,19217],{},[744,19218,19219],{},[747,19220,19221],{},"Problem Type",[11,19223,19224],{},"Binary classification.",[741,19226,19227],{},[744,19228,19229],{},[747,19230,19231],{},"Key Business Metric",[11,19233,19234],{},"Accuracy (i.e., the overall success rate) is not enough. The important factors are:",[18492,19236,19239],{"className":19237,"code":19238,"language":18497},[18495],"Recall rate of churn users\nROI of the retention campaign\n",[18453,19240,19238],{"__ignoreMap":17390},[11,19242,19243],{},"Why? Because we want to accurately identify users who will cancel (recall) and ensure that the retention campaign is profitable (ROI).",[39,19245,17891],{"id":19246},"data-collection",[11,19248,19249],{},"Data was collected from:",[741,19251,19252],{},[744,19253,19254],{},[747,19255,19256],{},"Internal Sources",[18492,19258,19261],{"className":19259,"code":19260,"language":18497},[18495],"* Payment history\n* App usage frequency\n* Time since last login\n* Support tickets\n* Plan type\n* Payment method\n* Payment failure history\n",[18453,19262,19260],{"__ignoreMap":17390},[741,19264,19265],{},[744,19266,19267],{},[747,19268,19269],{},"Data Volume",[18492,19271,19274],{"className":19272,"code":19273,"language":18497},[18495],"* 18 months of historical data\n* 1.5 million monthly sign-ups\n* Final dataset: **95,000 unique users** with complete history\n",[18453,19275,19273],{"__ignoreMap":17390},[39,19277,19279],{"id":19278},"preprocessing","Preprocessing",[11,19281,19282],{},"Problems Detected:",[18492,19284,19287],{"className":19285,"code":19286,"language":18497},[18495],"* 7% null values ​​in \"last login\"\n* 3% duplicate records\n* Categorical variables with high cardinality (cities)\n",[18453,19288,19286],{"__ignoreMap":17390},[11,19290,19291],{},"Actions Taken:",[18492,19293,19296],{"className":19294,"code":19295,"language":18497},[18495],"* Imputation with median for numerical variables\n* Removal of duplicates\n* Grouping of infrequent cities as \"Other\"\n",[18453,19297,19295],{"__ignoreMap":17390},[11,19299,19300,19301],{},"Time spent on this stage: ",[747,19302,19303],{},"72% of the project",[39,19305,19307],{"id":19306},"exploratory-analysis","Exploratory Analysis",[11,19309,19310],{},"Key Findings:",[741,19312,19313],{},[744,19314,19315],{},[747,19316,19317],{},"Insight 1",[11,19319,19320],{},"Users who do not log in for 14 days have:",[18492,19322,19325],{"className":19323,"code":19324,"language":18497},[18495],"* 22% probability of churn\n\nvs.\n\n* 4% for recently active users\n",[18453,19326,19324],{"__ignoreMap":17390},[741,19328,19329],{},[744,19330,19331],{},[747,19332,19333],{},"Insight 2",[11,19335,19336],{},"Users with more than 2 payment failures in 60 days:",[18492,19338,19341],{"className":19339,"code":19340,"language":18497},[18495],"* 35% probability of Churn\n",[18453,19342,19340],{"__ignoreMap":17390},[741,19344,19345],{},[744,19346,19347],{},[747,19348,19349],{},"Insight 3",[11,19351,19352],{},"Users who opened more than 3 support tickets:",[18492,19354,19357],{"className":19355,"code":19356,"language":18497},[18495],"* 18% churn\n* Main cause: technical issues\n",[18453,19358,19356],{"__ignoreMap":17390},[11,19360,19361],{},"This changes our focus: it's not just a retention issue, but also a user experience and technical support issue. This data tells us that users who have technical problems or difficulties using the app are much more likely to cancel, suggesting that an effective retention campaign should also address these issues and improve the user experience.",[39,19363,19365],{"id":19364},"feature-engineering","Feature Engineering",[11,19367,19368],{},"Variables such as the following were created:",[741,19370,19371,19377,19383,19389,19395,19405],{},[744,19372,19373,19376],{},[18453,19374,19375],{},"days_since_last_login",": This is the number of days since the user last logged into the application. This variable is important because, as discovered in the exploratory analysis, users who don't log in for an extended period are more likely to cancel their subscription.",[744,19378,19379,19382],{},[18453,19380,19381],{},"number_of_payment_failures_60d",": This is the number of payment failures a user has experienced in the last 60 days. As discovered in the EDA, users with more than 2 payment failures in this period have a significantly higher probability of canceling their subscription.",[744,19384,19385,19388],{},[18453,19386,19387],{},"average_weekly_usage",": This is the average weekly usage of the application. This variable can help capture the user's level of engagement with the application, which can be an important indicator of their likelihood of canceling.",[744,19390,19391,19394],{},[18453,19392,19393],{},"customer_time_in_months",": Users who have been customers for longer periods may have a lower probability of canceling.",[744,19396,19397,19400,19401,19404],{},[18453,19398,19399],{},"support_tickets_90d",": This is the number of support tickets a user has opened in the last 90 days. Since it was discovered that users who open more than 3 support tickets have a higher probability of canceling, this variable can be an important indicator of churn risk. * ",[18453,19402,19403],{},"payment_failure_ratio = failures \u002F attempts",": This ratio can be a more accurate indicator of churn risk related to payment issues, as it takes into account both the number of failed payments and the total number of payment attempts.",[744,19406,19407,19408,19411],{},"Binary variable: ",[18453,19409,19410],{},"is_new_user (\u003C3 months)",": New users may have a different churn risk compared to older users, so this variable can help capture that difference.",[11,19413,19414],{},"Also created:",[18492,19416,19419],{"className":19417,"code":19418,"language":18497},[18495],"inactivity_risk = days_since_last_login × (1 \u002F average_usage)\n",[18453,19420,19418],{"__ignoreMap":17390},[11,19422,19423,19424,19427],{},"This composite variable can be a powerful indicator of churn risk, as it combines information about user inactivity (days since last login) with their engagement level (average weekly usage). A high ",[18453,19425,19426],{},"risk_inactivity"," value would indicate that a user has not logged in for a long time and has a low level of usage, which could be a strong indicator that they are at risk of canceling their subscription.",[39,19429,17909],{"id":19430},"model-training",[11,19432,19433],{},"Data was divided as follows:",[741,19435,19436,19439,19442],{},[744,19437,19438],{},"75% training",[744,19440,19441],{},"15% validation",[744,19443,19444],{},"10% testing",[11,19446,19447],{},"The following were tested:",[5935,19449,19450,19462],{},[5938,19451,19452],{},[5941,19453,19454,19457,19459],{},[5944,19455,19456],{},"Model",[5944,19458,18853],{},[5944,19460,19461],{},"Recall churn",[5951,19463,19464,19475,19485,19496],{},[5941,19465,19466,19469,19472],{},[5956,19467,19468],{},"Logistic Regression",[5956,19470,19471],{},"0.76",[5956,19473,19474],{},"0.58",[5941,19476,19477,19479,19482],{},[5956,19478,18804],{},[5956,19480,19481],{},"0.84",[5956,19483,19484],{},"0.71",[5941,19486,19487,19490,19493],{},[5956,19488,19489],{},"XGBoost",[5956,19491,19492],{},"0.87",[5956,19494,19495],{},"0.78",[5941,19497,19498,19501,19504],{},[5956,19499,19500],{},"Neural Network",[5956,19502,19503],{},"0.85",[5956,19505,19506],{},"0.73",[11,19508,19509,19510,19512],{},"Although XGBoost had better metrics, ",[747,19511,18804],{}," was initially chosen because:",[741,19514,19515,19518,19521],{},[744,19516,19517],{},"It was more interpretable",[744,19519,19520],{},"Lower risk of overfitting",[744,19522,19523],{},"Easier to maintain",[11,19525,19526,19527,127],{},"This is key: ",[747,19528,19529],{},"the best metric is not always the best business decision",[39,19531,17382],{"id":18012},[11,19533,19534],{},"In the test set:",[741,19536,19537,19540,19543],{},[744,19538,19539],{},"9% actual churn",[744,19541,19542],{},"Model detected 76% of churns",[744,19544,19545],{},"False positives: 18%",[11,19547,19548],{},"Simulation:",[11,19550,19551],{},"Intervention is only implemented for users with a probability > 0.65.",[11,19553,19554],{},"Users marked as \"high risk\": 11,000",[11,19556,19557],{},"Of those:",[741,19559,19560,19563],{},[744,19561,19562],{},"6,800 were actually going to cancel",[744,19564,19565],{},"4,200 were false positives",[11,19567,19568],{},"Campaign cost:",[11,19570,19571,19572,19609],{},"11,000 × ",[50,19573,19575,19591],{"className":19574},[53],[50,19576,19578],{"className":19577},[57],[59,19579,19580],{"xmlns":61},[63,19581,19582,19588],{},[66,19583,19584,19586],{},[84,19585,111],{},[69,19587,1069],{},[157,19589,19590],{"encoding":159},"2 = ",[50,19592,19594],{"className":19593,"ariaHidden":89},[164],[50,19595,19597,19600,19603,19606],{"className":19596},[168],[50,19598],{"className":19599,"style":8734},[172],[50,19601,111],{"className":19602},[182],[50,19604],{"className":19605,"style":699},[244],[50,19607,1069],{"className":19608},[703],"22,000",[11,19611,19612],{},"Customers saved (campaign success rate 40%):",[11,19614,19615],{},"6,800 × 40% = 2,720 retained customers",[11,19617,19618],{},"Monthly revenue recovered:",[11,19620,19621,19622,19659],{},"2,720 × ",[50,19623,19625,19641],{"className":19624},[53],[50,19626,19628],{"className":19627},[57],[59,19629,19630],{"xmlns":61},[63,19631,19632,19638],{},[66,19633,19634,19636],{},[84,19635,17771],{},[69,19637,1069],{},[157,19639,19640],{"encoding":159},"25 = ",[50,19642,19644],{"className":19643,"ariaHidden":89},[164],[50,19645,19647,19650,19653,19656],{"className":19646},[168],[50,19648],{"className":19649,"style":8734},[172],[50,19651,17771],{"className":19652},[182],[50,19654],{"className":19655,"style":699},[244],[50,19657,1069],{"className":19658},[703],"68,000",[11,19661,19662],{},"Monthly ROI:",[11,19664,19665,19713],{},[50,19666,19668,19689],{"className":19667},[53],[50,19669,19671],{"className":19670},[57],[59,19672,19673],{"xmlns":61},[63,19674,19675,19686],{},[66,19676,19677,19680,19682,19684],{},[84,19678,19679],{},"68",[69,19681,90],{"separator":89},[84,19683,9101],{},[69,19685,2587],{},[157,19687,19688],{"encoding":159},"68,000 - ",[50,19690,19692],{"className":19691,"ariaHidden":89},[164],[50,19693,19695,19698,19701,19704,19707,19710],{"className":19694},[168],[50,19696],{"className":19697,"style":9207},[172],[50,19699,19679],{"className":19700},[182],[50,19702,90],{"className":19703},[240],[50,19705],{"className":19706,"style":245},[244],[50,19708,9101],{"className":19709},[182],[50,19711,2587],{"className":19712},[182],"22,000 = $46,000 net profit",[11,19715,19716],{},"Goal achieved.",[39,19718,17925],{"id":19719},"implementation",[11,19721,19722],{},"The model was deployed as:",[741,19724,19725,19728,19731,19734],{},[744,19726,19727],{},"REST API on FastAPI",[744,19729,19730],{},"Docker container",[744,19732,19733],{},"Nightly job that recalculates daily risk",[744,19735,19736],{},"CRM integration to trigger automated campaigns",[11,19738,19739],{},"Inference time per user: 12 ms",[39,19741,19743],{"id":19742},"production-monitoring","Production Monitoring",[11,19745,19746],{},"After 4 months:",[11,19748,19749],{},"The churn rate rose again to 7.4%.",[11,19751,19752],{},"The following were detected:",[741,19754,19755,19758],{},[744,19756,19757],{},"New competitor with aggressive discounts",[744,19759,19760],{},"Change in the behavior of younger users",[11,19762,19763,19766],{},[747,19764,19765],{},"Concept drift"," was identified; that is, the model was no longer accurately capturing churn patterns due to changes in the market and user behavior.",[11,19768,19769],{},"Retraining was performed using recent data.",[11,19771,19772],{},"New model:",[741,19774,19775,19778],{},[744,19776,19777],{},"Improved recall to 81%",[744,19779,19780],{},"Reduced churn again to 6.2%",[28,19782],{},[11,19784,19785,19786,19789],{},"What can we learn from this case? First, ",[747,19787,19788],{},"the model wasn't the focus—the process was",". Success didn't come from a sophisticated algorithm, but from a well-executed process that included:",[741,19791,19792,19795,19798],{},[744,19793,19794],{},"Good EDA (Engineering Development Analysis)",[744,19796,19797],{},"Good feature engineering",[744,19799,19800],{},"Correctly defining the business metrics",[11,19802,19803],{},"Second, accuracy wasn't the right metric. At this point, we needed to focus on ROI, because we didn't just want a model that performed well in technical metrics, but one that also generated a positive impact on the business.",[11,19805,19806,19807,19810],{},"As we've already mentioned, ",[747,19808,19809],{},"the model is part of a system"," that includes other components such as marketing, CRM, infrastructure, monitoring, and retraining. The project's success depends on the effective integration of all these components, not just the model itself.",[11,19812,19813,19814,19817],{},"Third, the project never ends; ",[747,19815,19816],{},"it's a continuous cycle",". Monitoring and maintenance are just as important as the initial training because the environment changes, users change, the market changes, and the model must adapt to remain effective.",[28,19819],{},{"title":17390,"searchDepth":11114,"depth":11114,"links":19821},[19822,19823,19824,19825,19828,19838,19839,19844,19845,19846],{"id":18148,"depth":10446,"text":18149},{"id":18210,"depth":10446,"text":18211},{"id":18246,"depth":10446,"text":18247},{"id":18256,"depth":10446,"text":18257,"children":19826},[19827],{"id":18351,"depth":11114,"text":18352},{"id":18381,"depth":10446,"text":18382,"children":19829},[19830,19831,19832,19833,19834,19835,19836,19837],{"id":18417,"depth":11114,"text":18418},{"id":18478,"depth":11114,"text":18479},{"id":18538,"depth":11114,"text":18539},{"id":18588,"depth":11114,"text":18589},{"id":18636,"depth":11114,"text":18637},{"id":18668,"depth":11114,"text":18669},{"id":18697,"depth":11114,"text":18698},{"id":18720,"depth":11114,"text":18721},{"id":18747,"depth":10446,"text":18748},{"id":18826,"depth":10446,"text":18827,"children":19840},[19841,19842,19843],{"id":18874,"depth":11114,"text":18875},{"id":18923,"depth":11114,"text":18924},{"id":18930,"depth":11114,"text":18931},{"id":18937,"depth":10446,"text":18938},{"id":18976,"depth":10446,"text":18977},{"id":19110,"depth":10446,"text":19111,"children":19847},[19848,19849,19850,19851,19852,19853,19854,19855,19856],{"id":19188,"depth":11114,"text":17885},{"id":19246,"depth":11114,"text":17891},{"id":19278,"depth":11114,"text":19279},{"id":19306,"depth":11114,"text":19307},{"id":19364,"depth":11114,"text":19365},{"id":19430,"depth":11114,"text":17909},{"id":18012,"depth":11114,"text":17382},{"id":19719,"depth":11114,"text":17925},{"id":19742,"depth":11114,"text":19743},"2026-04-18","\u002Fblog\u002Fworkflow-machine-learning-projects\u002Fshared\u002Fworkflow.webp",{},4,"\u002Fblog\u002Fblog\u002Fworkflow-machine-learning-projects",{"title":18075,"description":18080},{"loc":19864,"priority":17415,"lastmod":19857},"\u002Fblog\u002Fworkflow-machine-learning-projects","workflow-machine-learning-projects","blog\u002Fblog\u002Fworkflow-machine-learning-projects","Discover the typical workflow of a machine learning project, from data collection to model implementation, and learn about best practices and common challenges in the field of data science.",[17420,17423,19869,19870],"ML Projects","Workflow","MXgkwZT0KjAXfrhqOqfXLt24tkatVQk20fUyAE_f-Qw",1776805794165]