[学习分享] 数据挖掘作业与书籍The Elements of Statistical Learning Data Mining, [推广有奖]

Contents
Preface to the Second Edition vii
Preface to the First Edition xi
1 Introduction 1
2 Overview of Supervised Learning 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Variable Types and Terminology . . . . . . . . . . . . . . 9
2.3 Two Simple Approaches to Prediction:
Least Squares and Nearest Neighbors . . . . . . . . . . . 11
2.3.1 Linear Models and Least Squares . . . . . . . . 11
2.3.2 Nearest-Neighbor Methods . . . . . . . . . . . . 14
2.3.3 From Least Squares to Nearest Neighbors . . . . 16
2.4 Statistical Decision Theory . . . . . . . . . . . . . . . . . 18
2.5 Local Methods in High Dimensions . . . . . . . . . . . . . 22
2.6 Statistical Models, Supervised Learning
and Function Approximation . . . . . . . . . . . . . . . . 28
2.6.1 A Statistical Model
for the Joint Distribution Pr(X, Y ) . . . . . . . 28
2.6.2 Supervised Learning . . . . . . . . . . . . . . . . 29
2.6.3 Function Approximation . . . . . . . . . . . . . 29
2.7 Structured Regression Models . . . . . . . . . . . . . . . 32
2.7.1 Difficulty of the Problem . . . . . . . . . . . . . 32
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2.8 Classes of Restricted Estimators . . . . . . . . . . . . . . 33
2.8.1 Roughness Penalty and Bayesian Methods . . . 34
2.8.2 Kernel Methods and Local Regression . . . . . . 34
2.8.3 Basis Functions and Dictionary Methods . . . . 35
2.9 Model Selection and the Bias–Variance Tradeoff . . . . . 37
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 39
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3 Linear Methods for Regression 43
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 Linear Regression Models and Least Squares . . . . . . . 44
3.2.1 Example: Prostate Cancer . . . . . . . . . . . . 49
3.2.2 The Gauss–Markov Theorem . . . . . . . . . . . 51
3.2.3 Multiple Regression
from Simple Univariate Regression . . . . . . . . 52
3.2.4 Multiple Outputs . . . . . . . . . . . . . . . . . 56
3.3 Subset Selection . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.1 Best-Subset Selection . . . . . . . . . . . . . . . 57
3.3.2 Forward- and Backward-Stepwise Selection . . . 58
3.3.3 Forward-Stagewise Regression . . . . . . . . . . 60
3.3.4 Prostate Cancer Data Example (Continued) . . 61
3.4 Shrinkage Methods . . . . . . . . . . . . . . . . . . . . . . 61
3.4.1 Ridge Regression . . . . . . . . . . . . . . . . . 61
3.4.2 The Lasso . . . . . . . . . . . . . . . . . . . . . 68
3.4.3 Discussion: Subset Selection, Ridge Regression
and the Lasso . . . . . . . . . . . . . . . . . . . 69
3.4.4 Least Angle Regression . . . . . . . . . . . . . . 73
3.5 Methods Using Derived Input Directions . . . . . . . . . 79
3.5.1 Principal Components Regression . . . . . . . . 79
3.5.2 Partial Least Squares . . . . . . . . . . . . . . . 80
3.6 Discussion: A Comparison of the Selection
and Shrinkage Methods . . . . . . . . . . . . . . . . . . . 82
3.7 Multiple Outcome Shrinkage and Selection . . . . . . . . 84
3.8 More on the Lasso and Related Path Algorithms . . . . . 86
3.8.1 Incremental Forward Stagewise Regression . . . 86
3.8.2 Piecewise-Linear Path Algorithms . . . . . . . . 89
3.8.3 The Dantzig Selector . . . . . . . . . . . . . . . 89
3.8.4 The Grouped Lasso . . . . . . . . . . . . . . . . 90
3.8.5 Further Properties of the Lasso . . . . . . . . . . 91
3.8.6 Pathwise Coordinate Optimization . . . . . . . . 92
3.9 Computational Considerations . . . . . . . . . . . . . . . 93
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 94
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Contents xv
4 Linear Methods for Classification 101
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.2 Linear Regression of an Indicator Matrix . . . . . . . . . 103
4.3 Linear Discriminant Analysis . . . . . . . . . . . . . . . . 106
4.3.1 Regularized Discriminant Analysis . . . . . . . . 112
4.3.2 Computations for LDA . . . . . . . . . . . . . . 113
4.3.3 Reduced-Rank Linear Discriminant Analysis . . 113
4.4 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . 119
4.4.1 Fitting Logistic Regression Models . . . . . . . . 120
4.4.2 Example: South African Heart Disease . . . . . 122
4.4.3 Quadratic Approximations and Inference . . . . 124
4.4.4 L1 Regularized Logistic Regression . . . . . . . . 125
4.4.5 Logistic Regression or LDA? . . . . . . . . . . . 127
4.5 Separating Hyperplanes . . . . . . . . . . . . . . . . . . . 129
4.5.1 Rosenblatt’s Perceptron Learning Algorithm . . 130
4.5.2 Optimal Separating Hyperplanes . . . . . . . . . 132
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 135
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5 Basis Expansions and Regularization 139
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.2 Piecewise Polynomials and Splines . . . . . . . . . . . . . 141
5.2.1 Natural Cubic Splines . . . . . . . . . . . . . . . 144
5.2.2 Example: South African Heart Disease (Continued)146
5.2.3 Example: Phoneme Recognition . . . . . . . . . 148
5.3 Filtering and Feature Extraction . . . . . . . . . . . . . . 150
5.4 Smoothing Splines . . . . . . . . . . . . . . . . . . . . . . 151
5.4.1 Degrees of Freedom and Smoother Matrices . . . 153
5.5 Automatic Selection of the Smoothing Parameters . . . . 156
5.5.1 Fixing the Degrees of Freedom . . . . . . . . . . 158
5.5.2 The Bias–Variance Tradeoff . . . . . . . . . . . . 158
5.6 Nonparametric Logistic Regression . . . . . . . . . . . . . 161
5.7 Multidimensional Splines . . . . . . . . . . . . . . . . . . 162
5.8 Regularization and Reproducing Kernel Hilbert Spaces . 167
5.8.1 Spaces of Functions Generated by Kernels . . . 168
5.8.2 Examples of RKHS . . . . . . . . . . . . . . . . 170
5.9 Wavelet Smoothing . . . . . . . . . . . . . . . . . . . . . 174
5.9.1 Wavelet Bases and the Wavelet Transform . . . 176
5.9.2 Adaptive Wavelet Filtering . . . . . . . . . . . . 179
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 181
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Appendix: Computational Considerations for Splines . . . . . . 186
Appendix: B-splines . . . . . . . . . . . . . . . . . . . . . 186
Appendix: Computations for Smoothing Splines . . . . . 189
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6 Kernel Smoothing Methods 191
6.1 One-Dimensional Kernel Smoothers . . . . . . . . . . . . 192
6.1.1 Local Linear Regression . . . . . . . . . . . . . . 194
6.1.2 Local Polynomial Regression . . . . . . . . . . . 197
6.2 Selecting the Width of the Kernel . . . . . . . . . . . . . 198
6.3 Local Regression in IRp . . . . . . . . . . . . . . . . . . . 200
6.4 Structured Local Regression Models in IRp . . . . . . . . 201
6.4.1 Structured Kernels . . . . . . . . . . . . . . . . . 203
6.4.2 Structured Regression Functions . . . . . . . . . 203
6.5 Local Likelihood and Other Models . . . . . . . . . . . . 205
6.6 Kernel Density Estimation and Classification . . . . . . . 208
6.6.1 Kernel Density Estimation . . . . . . . . . . . . 208
6.6.2 Kernel Density Classification . . . . . . . . . . . 210
6.6.3 The Naive Bayes Classifier . . . . . . . . . . . . 210
6.7 Radial Basis Functions and Kernels . . . . . . . . . . . . 212
6.8 Mixture Models for Density Estimation and Classification 214
6.9 Computational Considerations . . . . . . . . . . . . . . . 216
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 216
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
7 Model Assessment and Selection 219
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 219
7.2 Bias, Variance and Model Complexity . . . . . . . . . . . 219
7.3 The Bias–Variance Decomposition . . . . . . . . . . . . . 223
7.3.1 Example: Bias–Variance Tradeoff . . . . . . . . 226
7.4 Optimism of the Training Error Rate . . . . . . . . . . . 228
7.5 Estimates of In-Sample Prediction Error . . . . . . . . . . 230
7.6 The Effective Number of Parameters . . . . . . . . . . . . 232
7.7 The Bayesian Approach and BIC . . . . . . . . . . . . . . 233
7.8 Minimum Description Length . . . . . . . . . . . . . . . . 235
7.9 Vapnik–Chervonenkis Dimension . . . . . . . . . . . . . . 237
7.9.1 Example (Continued) . . . . . . . . . . . . . . . 239
7.10 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . 241
7.10.1 K-Fold Cross-Validation . . . . . . . . . . . . . 241
7.10.2 The Wrong and Right Way
to Do Cross-validation . . . . . . . . . . . . . . . 245
7.10.3 Does Cross-Validation Really Work? . . . . . . . 247
7.11 Bootstrap Methods . . . . . . . . . . . . . . . . . . . . . 249
7.11.1 Example (Continued) . . . . . . . . . . . . . . . 252
7.12 Conditional or Expected Test Error? . . . . . . . . . . . . 254
Bibliographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . 257
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257