《An.Introduction.to.Machine.Learning.with.Application.in.R》.pdf

M I C H A E L C L A R K C E N T E R F O R S O C I A L R E S E A R C H U N I V E R S I T Y O F N O T R E D A M E A 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 G W I T H A P P L I C AT I O N S I N R Machine Learning 2 Contents Preface 5 Introduction: Explanation Prediction 6 Some Terminology 7 Tools You Already Have 7 The Standard Linear Model 7 Logistic Regression 8 Expansions of Those Tools 9 Generalized Linear Models 9 Generalized Additive Models 9 The Loss Function 10 Continuous Outcomes 10 Squared Error 10 Absolute Error 10 Negative Log-likelihood 10 R Example 11 Categorical Outcomes 11 Misclassification 11 Binomial log-likelihood 11 Exponential 12 Hinge Loss 12 Regularization 12 R Example 13 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 Accuracy: Other Measures of Performance 18 Process Overview 20 Data Preparation 20 Define Data and Data Partitions 20 Feature Scaling 21 Fe