11 Tree-Based Methods, Bagging, Boosting.7 Bootstrapping Standard Errors and Confidence Intervals.6.9 Ridge, LASSO, and Elastic net models for other Y distributions.6.2 Intuitions about Penalized Cost Functions and Regularization.4.13 Data Exploration in a Nested World….4.12 Alternative Performance Metrics in \(train()\).4.8 Using CV to Select Best Model Configurations.4.2 The single validation (hold-out test) set approach.3.9 Classification model performance metrics.3.7 Comparisons between these four classifiers.3 Introduction to Classification Models.2.3.1 Other Regression Performance Metrics.2.3 Combining Sets of Predictors Using \(model.matix()\).2.2.3 Extension to Categorical Predictors.2.2.2 Extension to Multiple Linear Regression.1.4 An Empirical Demonstration of Overfitting and the Bias-Variance Trade-off.1.3.4 How do we assess model performance?.1.3.2 More details on supervised techniques.1.3.1 An introductory framework for machine learning.1.3 Concepts and Definitions (Chapters 1
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