The Machine Learning Simplified: A Gentle Introduction to Supervised Learning

111 Pages·16.709 MB·English

by  Andrew Wolf

Copyright(cid:13)c 2022AndrewWolf
MACHINE LEARNING SIMPLIFIED: A GENTLE INTRODUCTION TO SUPERVISED LEARNING
ANDREW WOLF
THEMLSBOOK.COM
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1.0.1
Firstrelease,January2022
Contents
I FUNDAMENTALS OF SUPERVISED LEARNING
1 Introduction .................................................... 5
1.1 Machine Learning 6
1.1.1 SupervisedLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.1.2 UnsupervisedLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 Machine Learning Pipeline 9
1.2.1 DataScience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2.2 MLOperations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Artificial Intelligence 11
1.3.1 InformationProcessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.2 TypesofAI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4 Overview of this Book 13
2 Overview of Supervised Learning ............................... 15
2.1 ML Pipeline: Example 15
2.1.1 ProblemRepresentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.2 LearningaPredictionFunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.3 HowGoodisourPredictionFunction? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.1.4 ControllingModelComplexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 ML Pipeline: General Form 23
2.2.1 DataExtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.2 DataPreparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.3 ModelBuilding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.4 ModelDeployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3 Model Learning ............................................... 29
3.1 Linear Regression 29
3.1.1 LinearModels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1.2 Goodness-of-Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1.3 GradientDescentAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1.4 GradientDescentwithMoreParameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Gradient Descent in Other ML Models 43
3.2.1 GettingStuckinaLocalMinimum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.2 OvershootingGlobalMinimum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.3 Non-differentiableCostFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4 Basis Expansion and Regularization ............................. 49
4.1 Basis Expansion 49
4.1.1 PolynomialBasisExpansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.2 ComparisonofModelWeights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2 Regularization 55
4.2.1 RidgeRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2.2 ChoosingRegularizationStrengthλ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.3 LassoRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.4 ComparisonbetweenL1andL2Regularization . . . . . . . . . . . . . . . . . . . . . . . . . 57
5 Model Selection ............................................... 59
5.1 Bias-Variance Decomposition 59
5.1.1 MathematicalDefinition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.1.2 DiagnosingBiasandVarianceErrorSources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Validation Methods 64
5.2.1 Hold-outValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5.2.2 CrossValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.3 Unrepresentative Data 68
6 Feature Selection .............................................. 71
6.1 Introduction 71
6.2 Filter Methods 73
6.2.1 UnivariateSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.2.2 MultivariateSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 Search Methods 76
6.4 Embedded Methods 76
6.5 Comparison 77
7 Data Preparation .............................................. 79
7.1 Data Cleaning 80
7.1.1 DirtyData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
7.1.2 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
7.2 Feature Transformation 83
7.2.1 FeatureEncoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
7.2.2 FeatureScaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
7.3 Feature Engineering 87
7.3.1 FeatureBinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.3.2 RatioFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.4 Handling Class Label Imbalance 90
7.4.1 Oversampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.4.2 SyntheticMinorityOversamplingTechnique(SMOTE) . . . . . . . . . . . . . . . . . . . . . 92
A Appendix Unsupervised Learning ................................ e
B Appendix Non-differentiable Cost Functions ..................... g
B.0.1 DiscontinuousFunctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . g
B.0.2 ContinuousNon-differentiableFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

i
PREFACE
It could be said that machine learning is my life. I am a machine
learningengineerbydayandanenthusiasticSTEMtutorbynight.
Iamconsistentlyinspiredbythisinfinitelyexcitingfieldandithas
become one of my greatest passions. My interest in the machine
learningdatesbackto2012whenIcameacrossanarticledescribing
amachinelearningexperimentconductedbytheGoogleBrainteam.
Theteam,ledbyAndrewNgandJeffDean,createdaneuralnetwork
thatlearnedtorecognizecatsbywatchingimagestakenfromframes
ofYouTubevideos. IbegantoconsiderthepossibilitiesandIwas
hooked.
WhyIWroteThisBook
I,forone,eagerlylookforwardtoafutureinwhichMLwillblossom
and reveal its full potential. However, in my conversations with
friends and colleagues outside the ML field, I’ve observed that
they are often perplexed by the seeming complexity of it. Many
ofthemareintriguedbythefieldandwanttolearnmore,butfind
a dearth of clear, reliable resources on the internet. Sources are
either rife with academic trilogies filled with theorems designed
forexperiencedresearchersandprofessionals(Icouldn’tevenget
through half of one) or are sprinkled with fishy fairy tales about
artificialintelligence,data-sciencemagic,andjobsofthefuture.
Thisbookisdedicatedtothem—andthousandsmore-whowant
to truly understand the methods and use cases of ML both from
conceptualandmathematicalpointsofview,butwhomaynothave
the luxury of time, which is required to comb through thousands
of hours of technical literature, full of intimidating formulas and
academicjargon.
WhatThisBookIsAbout
My goal for this book is to help make machine learning available
toasmanypeopleaspossiblewhethertechnicalornot. Itiseasily
accessible fora non-technical reader, but also containsway enough
mathematicaldetail toserveas anintroduction tomachine learning
for a technical reader. Nevertheless, some prior knowledge of
mathematics, statistics and the Python programming language is
recommendedtogetthemostoutofthisbook.
I’vedonemybesttomakethisbookbothcomprehensiveandfun
to read — mind you, that’s no easy feat! I’ve worked to combine
mathematical rigor with simple, intuitive explanations based on
examples from oureverydaylives. For example, deciding what to
do over the weekend, or guessing a friend’s favorite color based
onsomethingliketheirheightandweight(Iamonlyhalf-kidding
1
here). Youwillfindanswerstothesequestionsandmanymoreas
youreadthisbook.
HowtoUseThisBook
Thisbookisdividedintotwoparts. PartIdiscussesthefundamen-
tals of (supervised) machine learning, and Part II discusses more
advanced machine learning algorithms. I divided the book in this
wayforaveryimportantreason. Onemistakemanystudentsmake
istojumprightintothealgorithms(oftenafterhearingoneoftheir
names,likeSupportVectorMachines)withoutaproperfoundation.
In doing so, they often fail to understand, or misunderstand, the
algorithms. Some of these students get frustrated and quit after
thisexperience. Inwritingthisbook,Iassumedthechapterswould
be read sequentially. The book has a specific story line and most
explanationsappearinthetextonlyoncetoavoidredundancy.
Ihavealso supplemented thisbookwith aGitHubrepositorythat
containspythonimplementationsofconceptsexplainedinthebook.
Formoreinformation, scantheQRcodelocatedin the‘TryIt Now’
boxattheendofeachchapter,orjustgodirectlyto
github.com/5x12/themlsbook.
FinalWords
Hopefullythisbookpersuadesyouthatmachinelearningisnotthe
intimidating technology that it initially appears to be. Whatever
your background and aspirations, you will find this book a useful
introductiontothisfascinatingfield.
Shouldyouhaveanyquestionsorsuggestions,feelfreetoreachout
tomeatawolf.io. Iappreciateyourfeedback,andIhopethatitwill
makethefutureeditionsofthisbookevenmorevaluable.
Goodluckinyourmachinelearningjourney,
Yourauthor

Part I
FUNDAMENTALS OF SUPERVISED
LEARNING