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 Learning2ContentsPreface5Introduction: Explanation & Prediction6Some Terminology7Tools You Already Have7The Standard Linear Model7Logistic Regression8Expansions of Those Tools9Generalized Linear Models9Generalized Additive Models9The Loss Function10Continuous Outcomes10Squared Error10Absolute Error10Negative Log-likelihood10R Example11Categorical Outcomes11Misclassification11Binomial log-likelihood11Exponential12Hinge Loss12Regularization12R Example133Applications in RBias-Variance Tradeoff14Bias & Variance14The Tradeoff15Diagnosing Bias-Variance Issues & Possible Solutions16Worst Case Scenario16High Variance16High Bias16Cross-Validation16Adding Another Validation Set17K-fold Cross-Validation17Leave-one-out Cross-Validation17Bootstrap18Other
Brief Glossary of Common Terms 42. 5 Applications in R Preface The purpose of this document is to provide a conceptual introduction to statistical or machine learning (ML) techniques for those that might ... An Introduction to Machine Learning with Applications in R
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