Chapter 5. Resampling Methods 5.1. Cross-Validation 5.1.1. The Validation Set Approach 5.1.2. Leave-One-Out Cross-Validation 5.1.3. k-Fold Cross-Validation 5.1.4. Bias-Variance Trade-Off for k-Fold Cross-Validation 5.1.5. Cross-Validation on Classification Problems 5.2. The Bootstrap Chapter 5. Resampling Methods repeatedly drawing samples from a training set and refitting a...

Chapter 4. Classification 4.1. Overview of Classification 4.2. Why Not Linear Regression? 4.3. Logistic Regression 4.3.1. The Logistic Model 4.3.2. Estimating the Regression Coefficients: MLE 4.3.3. Multinomial Logistic Regression 4.4. Generative Models for Classification 4.4.1. LDA: Linear Discriminant Analysis for $p = 1$ 4.4.2. LDA:...

Chapter 3. Linear Regression 3.1. Simple Linear Regression 3.1.1. Estimating the Coefficients 3.1.2. Assessing the Accuracy of the Coefficient Estimates 3.1.3. Assessing the Accuracy of the Model Residual Standard Error (RSE) $R^2$ Statistics 3.2. Multiple Linear Regression 3.2.1. Estimating the Regression Coefficients Ordinary Least Squares...

Chapter 2. Statistical Learning 2.1. What is Statistical Learning? 2.1.1. Why Estimate $f$ ? Prediction Inference 2.1.2. Estimating $f$ : By Using Training Data Parametric Methods: Model-based approach Non-parametric Methods: Data-driven approach Example of Nonparametric model: KNN Reg. 2.1.3. Trade-off between Prediction Accuray & Interpretability...