Transcription of AN INTRODUCTION TO MACHINE LEARNING
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AN INTRODUCTION TO MACHINE LEARNING
M I C H A E L C L A R KC E N T E R F O R S O C I A L R E S E A R C HU N I V E R S I T Y O F N O T R E D A M EA N I N T R O D U C T I O N T O M A C H I N E L E A R N I N GW I T H A P P L I C AT I O N S I N RMachine Learning2 ContentsPreface5 INTRODUCTION : Explanation & Prediction6 Some Terminology7 Tools You Already Have7 The Standard Linear Model7 Logistic Regression8 Expansions of Those Tools9 Generalized Linear Models9 Generalized Additive Models9 The Loss Function10 Continuous Outcomes10 Squared Error10 Absolute Error10 Negative Log-likelihood10R Example11 Categorical Outcomes11 Misclassification11 Binomial log-likelihood11 Exponential12 Hinge Loss12 Regularization12R Example133 Applications in RBias-Variance Tradeoff14 Bias & Variance14 The Tradeoff15 Diagnosing Bias-Variance Issues & Possible Solutions16 Worst Case Scenario16 High Variance16 High Bias16 Cross-Validation16 Adding Another Validation Set17K-fold Cross-Validation17 Leave-one-out Cross-Validation17
3 Applications in R Bias-Variance Tradeoff 14 Bias & Variance 14 The Tradeoff 15 Diagnosing Bias-Variance Issues & Possible Solutions 16 Worst Case Scenario 16 High Variance 16 High Bias 16 Cross-Validation 16 Adding Another Validation Set 17 K-fold Cross-Validation 17 Leave-one-out Cross-Validation 17 Bootstrap 18 Other Stuff 18 Model Assessment & Selection 18 Beyond Classification …
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