Learning from Data Made Easy with R: A Gentle Introduction for Data Science

201 Pages·1.964 MB·English

by  Lewis N.D.

Ego autem et domus
mea serviemus Domino.

LEARNING FROM DATA
MADE EASY
WITH R
A Gentle Introduction for Data Science.
Dr. N.D. Lewis
Copyright © 2016 by N.D. Lewis
All rights reserved. No part of this publication may be reproduced, dis-
tributed, or transmitted in any form or by any means, including photo-
copying, recording, or other electronic or mechanical methods, without
thepriorwrittenpermissionoftheauthor,exceptinthecaseofbriefquo-
tationsembodiedincriticalreviewsandcertainothernoncommercialuses
permitted by copyright law. For permission requests, contact the author
at: www.AusCov.com.
Disclaimer: Although the author and publisher have made every effort to
ensure that the information in this book was correct at press time, the
author and publisher do not assume and hereby disclaim any liability to
anypartyforanyloss,damage,ordisruptioncausedbyerrorsoromissions,
whether such errors or omissions result from negligence, accident, or any
other cause.
Ordering Information: Quantity sales. Special discounts are available on
quantity purchases by corporations, associations, and others. For details,
email: [email protected]
Image photography by Deanna Lewis
ISBN-13: 978-1523459735
ISBN-10: 1523459735
Contents
Acknowledgements iii
Preface viii
How to Get the Most from this Book 1
1 The Learning Problem in a Nutshell 5
TheABCsofInductiveandDeductiveInference . . . . . . . . . . . . . . . . 6
ThreeKeyElementsoftheLearningProblem . . . . . . . . . . . . . . . . . . 9
TheGoalofLearningfromData . . . . . . . . . . . . . . . . . . . . . . . . . 11
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Supervised Learning 17
TheEssentialIngredientforEffectiveClassification . . . . . . . . . . . . . . . 18
TheAnswertoHowtoDeterminetheHypothesisClass . . . . . . . . . . . . 19
TheTwoCoreApproachestoSupervisedLearning . . . . . . . . . . . . . . . 21
WhatisaBayesClassifier? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
TwoEasyTrickstoEstimatetheBayesError . . . . . . . . . . . . . . . . . . 25
HowtoUnleashthePoweroftheNaiveBayesClassifier . . . . . . . . . . . . 27
IncrediblySimplyWaystoBuildtheNaiveBayesClassifierinR . . . . . . . 31
HowtoLeveragetheValueofthek-NearestNeighborsAlgorithm . . . . . . . 41
AStraightforwardWaytoUsek-NearestNeighborsinR . . . . . . . . . . . . 45
TheKeytoLinearDiscriminantAnalysis . . . . . . . . . . . . . . . . . . . . 52
EssentialElementsforDiscriminantAnalysisinR . . . . . . . . . . . . . . . 55
TheSecretofClassificationwithLogisticRegression . . . . . . . . . . . . . . 61
TheEasyWaytoBuildaLogisticRegressionClassifierinR. . . . . . . . . . 64
ADamnGoodIdeatoInspireYourCreativityandPassion . . . . . . . . . . 70
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3 Unsupervised Learning 79
UnsupervisedLearninginaNutshell . . . . . . . . . . . . . . . . . . . . . . . 79
TheTwoCoreApproachesandHowTheyWork . . . . . . . . . . . . . . . . 80
TechniquesthatCrushUnsupervisedLearning&HowtoBuildtheminR . . 81
AFantasticWinningExampleofUnsupervisedLearningYouCanEmulate . 99
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
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4 Semi Supervised Learning 107
HowCanUnlabeledDataCanHelp? . . . . . . . . . . . . . . . . . . . . . . . 108
TheConsistencyAssumption . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
ASuperSimpleWaytoTrySemi-supervisedLearning . . . . . . . . . . . . . 111
TheSelfLearningAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Semi-SupervisedModelBasedLearningwithR . . . . . . . . . . . . . . . . . 115
MasterthisPracticalIllustrationusingLandClassification . . . . . . . . . . 120
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5 Statistical Learning Theory 127
TheVapnik-ChervonenkisGeneralizationBound . . . . . . . . . . . . . . . . 128
WhatistheVapnik-ChervonenkisDimension?. . . . . . . . . . . . . . . . . . 130
TheKeytoStructuralRiskMininization. . . . . . . . . . . . . . . . . . . . . 133
TheBestAdviceonUsingStatisticalLearningTheoryinPractice . . . . . . 134
HowtoMastertheSupportVectorMachine . . . . . . . . . . . . . . . . . . 136
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6 Model Selection 145
HowIImprovedMyModelsinOneEvening. . . . . . . . . . . . . . . . . . . 146
ALittleMistakethatCost$5Million . . . . . . . . . . . . . . . . . . . . . . 147
ThreeKeyLessonsoftheNoFreeLunchTheorem . . . . . . . . . . . . . . . 149
WhatistheBiasVarianceTradeoff? . . . . . . . . . . . . . . . . . . . . . . . 152
DoYouMakeThisMistakewithYourModels? . . . . . . . . . . . . . . . . . 156
TheSecretoftheHoldOutTechnique . . . . . . . . . . . . . . . . . . . . . . 158
TheArtofEffectiveCrossValidation. . . . . . . . . . . . . . . . . . . . . . . 159
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Index 177
Dedicated to Angela, wife, friend and mother
extraordinaire.

Acknowledgments
A special thank you to:
My wife Angela, for her patience and constant encouragement.
My daughter Deanna, for taking hundreds of photographs for
this book and my website.
And the readers of my earlier books who contacted me with
questions and suggestions.
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