Table Of Content

Ego autem et domus mea serviemus Domino. MACHINE LEARNING MADE EASY With R An Intuitive Step by Step Blueprint for Beginners Dr. N.D Lewis Copyright © 2017 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: info@NigelDLewis.com Image photography by Deanna Lewis with helpful assistance from Naomi Lewis. ISBN-13: 978-1546483755 ISBN-10: 1546483756 Contents Acknowledgements iii Preface viii 1 Introduction to Machine Learning 1 WhoisthisBookfor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 WhatisMachineLearning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 HowtoGettheAbsoluteMostPossibleBenefitfromthisBook . . . . . . . . 5 GettingR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 LearningR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 UsingPackages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 EffectiveUseofFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Decision Trees 11 UnderstandingDecisionTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 TheDivideandConquerAlgorithm . . . . . . . . . . . . . . . . . . . . 14 UnderstandingEntropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 CalculatingEntropy-AStepbyStepExample . . . . . . . . . . . . . . 18 AdvantagesofDecisionTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 PracticalApplicationofDecisionTrees. . . . . . . . . . . . . . . . . . . . . . 24 IntelligentShoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 MicroRibonucleicAcid . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 TrafficAccidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Example-IdentifyingCounterfeitCurrency . . . . . . . . . . . . . . . . . . . 29 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 30 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 32 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 33 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 36 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 37 LimitationsofDecisionTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 k-Nearest Neighbors 47 UnderstandingNearestNeighbors. . . . . . . . . . . . . . . . . . . . . . . . . 48 MeasuringClosenesswithDistanceMetrics . . . . . . . . . . . . . . . . 48 AnExampletoBoostYourUnderstanding. . . . . . . . . . . . . . . . . 50 Advantagesofk-NearestNeighbors . . . . . . . . . . . . . . . . . . . . . 53 i PracticalApplicationofk-NearestNeighbors . . . . . . . . . . . . . . . . . . 53 LeafClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 ClassifyingHumanActivity . . . . . . . . . . . . . . . . . . . . . . . . . 55 IdentifyingAnimalBehavior. . . . . . . . . . . . . . . . . . . . . . . . . 55 Example-IdentifyingWineCultivars . . . . . . . . . . . . . . . . . . . 57 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 57 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 60 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 62 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 63 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 64 Limitationsofk-NearestNeighbors . . . . . . . . . . . . . . . . . . . . . . . . 65 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Naive Bayes Classifier 69 UnderstandingtheNaiveBayesClassifier . . . . . . . . . . . . . . . . . . . . 70 TheCoreConceptofBayesRule . . . . . . . . . . . . . . . . . . . . . . 70 TheBayesClassifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 CoreAssumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 AStepbyStepExampletoBuildYourIntuition . . . . . . . . . . . . . 74 AdvantagesoftheNaiveBayesClassifier . . . . . . . . . . . . . . . . . . . . . 77 PracticalApplicationoftheNaiveBayesClassifier . . . . . . . . . . . . . . . 77 GestureRecognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 MedicalResearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 PredictingTrafficCongestion . . . . . . . . . . . . . . . . . . . . . . . . 79 Example-ClassifyingSimulationData . . . . . . . . . . . . . . . . . . . . . 80 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 81 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 83 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 83 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 84 Step5-AssessTestSetPerformance . . . . . . . . . . . . . . . . . . . 86 Example-IdentifyingRadarSignals . . . . . . . . . . . . . . . . . . . . . . 87 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 87 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 90 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 92 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 93 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 94 LimitationsoftheNaiveBayesClassifier . . . . . . . . . . . . . . . . . . . . . 98 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Linear Discriminant Analysis 101 UnderstandingLinearDiscriminantAnalysis . . . . . . . . . . . . . . . . . . 102 AClarifyingIllustration . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 LinearDiscriminantAnalysisinaNutshell. . . . . . . . . . . . . . . . . 104 AdvantagesofLinearDiscriminantAnalysis . . . . . . . . . . . . . . . . 104 PracticalApplicationofLinearDiscriminantAnalysis . . . . . . . . . . . . . 105 FoodAuthentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 GradingChickenFillets . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 AutismSpectrumClassification . . . . . . . . . . . . . . . . . . . . . . . 108 Example-MolecularClassificationviaGeneExpression . . . . . . . . . . . . 109 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 109 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 113 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 117 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 122 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 124 LimitationsofLinearDiscriminantAnalysis . . . . . . . . . . . . . . . . . . . 126 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6 Linear Regression 129 UnderstandingLinearRegression . . . . . . . . . . . . . . . . . . . . . . . . . 130 SimpleLinearRegression . . . . . . . . . . . . . . . . . . . . . . . . . . 130 ClarifyingExpectedValue . . . . . . . . . . . . . . . . . . . . . . . . . . 131 ExplainingOrdinaryLeastSquares . . . . . . . . . . . . . . . . . . . . . 134 CoefficientofDetermination . . . . . . . . . . . . . . . . . . . . . . . . . 135 MultipleRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 AssumptionsoftheLinearRegressionModel . . . . . . . . . . . . . . . 138 AdvantagesofLinearRegression . . . . . . . . . . . . . . . . . . . . . . 139 PracticalApplicationofLinearRegression . . . . . . . . . . . . . . . . . . . . 140 SoilDensityPrediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 AgeFriendlyCities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 ForeignDirectInvestmentinNepal . . . . . . . . . . . . . . . . . . . . . 144 Example-PredictingtheLengthofPaleolithicHand-Axes . . . . . . . . . . 145 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 147 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 149 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 152 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . . 154 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 173 LimitationsofLinearRegression . . . . . . . . . . . . . . . . . . . . . . . . . 178 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 7 Logistic Regression 181 UnderstandingLogisticRegression . . . . . . . . . . . . . . . . . . . . . . . . 182 OddsRatio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 TheLogisticCurve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 MaximumLikelihoodEstimation . . . . . . . . . . . . . . . . . . . . . . 188 AsaBayesClassifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 AdvantagesofLogisticRegression. . . . . . . . . . . . . . . . . . . . . . 189 PracticalApplicationofLogisticRegression . . . . . . . . . . . . . . . . . . . 190 CustomerChurn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 OpinionMining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 ZoonoticDiseaseClassification . . . . . . . . . . . . . . . . . . . . . . . 193 Example-ClassifyingPurpleRockCrabs . . . . . . . . . . . . . . . . . . . . 194 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 194 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 197 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 197 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 201 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 210 LimitationsofLogisticRegression . . . . . . . . . . . . . . . . . . . . . . . . 218 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 8 Support Vector Machines 221 UnderstandingSupportVectorMachines. . . . . . . . . . . . . . . . . . . . . 222 ExplainingtheOptimalHyperplane . . . . . . . . . . . . . . . . . . . . 223 AdvantagesofSupportVectorMachines . . . . . . . . . . . . . . . . . . 226 PracticalApplicationofSupportVectorMachines . . . . . . . . . . . . . . . 226 ForecastingtheStockMarket . . . . . . . . . . . . . . . . . . . . . . . . 226 IdentificationofEarlyOnsetBreastCancer . . . . . . . . . . . . . . . . 227 SignatureAuthentication . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Example-ClassifyingDiabetes . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 229 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 233 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 234 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 239 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . . 240 LimitationsofSupportVectorMachines . . . . . . . . . . . . . . . . . . . . . 243 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 9 Random Forests 247 UnderstandingRandomForests . . . . . . . . . . . . . . . . . . . . . . . . . 248 TheRandomForestsAlgorithm . . . . . . . . . . . . . . . . . . . . . . . 248 AdvantagesofRandomForests . . . . . . . . . . . . . . . . . . . . . . . 250 PracticalApplicationofRandomForests. . . . . . . . . . . . . . . . . . . . . 250 Waste-waterDeteriorationModeling . . . . . . . . . . . . . . . . . . . . 251 GlaucomaPrediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 ObesityRiskFactors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 Example-ClassifyingThyroidStatus . . . . . . . . . . . . . . . . . . . . . . 252 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 253 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 255 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 255 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 255 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 257 LimitationsofRandomForests . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 10Boosting 263 UnderstandingBoosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 TheBasicApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 TheAdaboostAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 265 AdvantagesofBoosting . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 PracticalApplicationofBoosting . . . . . . . . . . . . . . . . . . . . . . . . . 268 ReverberationSuppression. . . . . . . . . . . . . . . . . . . . . . . . . . 268 BasketballPlayerDetection . . . . . . . . . . . . . . . . . . . . . . . . . 269 VehicleLogoRecognition . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Example-ClassifyingSonarSignals . . . . . . . . . . . . . . . . . . . . . . . 270 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 271 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 272 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 273 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 274 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 275 LimitationsofBoosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 11K- Means Clustering 279 UnderstandingK-MeansClustering . . . . . . . . . . . . . . . . . . . . . . . 280 HowK-meansClusteringWorks . . . . . . . . . . . . . . . . . . . . . . 281 AdvantagesofK-MeansClustering. . . . . . . . . . . . . . . . . . . . . 285 PracticalApplicationofK-MeansClustering . . . . . . . . . . . . . . . . . . 286 PinpointingtheRasberryCrazyAnt . . . . . . . . . . . . . . . . . . . . 286 AutomaticShags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 IdentifyingFibromyalgiaSubgroups . . . . . . . . . . . . . . . . . . . . 290 Example-Example-ClassifyingtheWell-beingofCitizens . . . . . . . . . . 292 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 293 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 295 Step3-TrainModelusingSampleData. . . . . . . . . . . . . . . . . . 296 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 297 Step5-ImprovingModelPerformance . . . . . . . . . . . . . . . . . . 298 LimitationsofK-MeansClustering. . . . . . . . . . . . . . . . . . . . . . . . 307 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 12Tips to Enhance Performance 309 ClarifyingOccam’sRazor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Usefulinterpretations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 ModelClarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 UnderstandingDataSnooping. . . . . . . . . . . . . . . . . . . . . . . . . . . 311 TheDangerofSnooping . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 TheBenefitofaValidationSet . . . . . . . . . . . . . . . . . . . . . . . 312 PracticalLessonsoftheNoFreeLunchTheorem . . . . . . . . . . . . . . . . 313 Don’tFallinLove . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 AllModelsAreLimited . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 WorkwithDomainExperts . . . . . . . . . . . . . . . . . . . . . . . . . 315 ExplainingtheBiasVarianceTrade-off . . . . . . . . . . . . . . . . . . . . . . 316 InterpretingReducibleError. . . . . . . . . . . . . . . . . . . . . . . . . 316 SimplifyingBias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 IlluminatingVariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 TheSecretoftheHoldOutTechnique . . . . . . . . . . . . . . . . . . . . . . 319 TheArtofEffectiveCrossValidation. . . . . . . . . . . . . . . . . . . . . . . 322 k-foldCrossValidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322 ChoosingtheNumberofFolds . . . . . . . . . . . . . . . . . . . . . . . 323 LeaveOneOutCrossValidation . . . . . . . . . . . . . . . . . . . . . . 323 Example-ClassifyingPurpleRockCrabswithNaiveBayes . . . . . . . . . . 324 Step1-CollectingandExploringtheData . . . . . . . . . . . . . . . . 324 Step2-PreparingtheData. . . . . . . . . . . . . . . . . . . . . . . . . 324 Step3-TrainModelusingTrainSet. . . . . . . . . . . . . . . . . . . . 325 Step4-EvaluateModelPerformance . . . . . . . . . . . . . . . . . . . 326 Step5-AssessingTestSetPerformance . . . . . . . . . . . . . . . . . . 327 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 SuggestedReading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 Index 336

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Finally, A Blueprint for Machine Learning with R!Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creat

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