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The Elements of Statistical Learning：data mining, inference, and prediction [Hardcover]
T. Hastie (Author), R. Tibshirani (Author), J. H. Friedman (Author)



Editorial Reviews
From the reviews:
SIAM REVIEW
"The book is very well written and color is used throughout. Color adds a dimension that can be used to help the reader visualize high-dimensional data, and it is also very useful to help the eye see patterns and clusters more easily. This makes color effective in the book and not just a pleasing gimmick. This is the first book of its kind to treat data mining from a statistical perspective that is comprehensive and up-to-date on the statistical methods…I found the book to be both innovative and fresh. It provides an important contribution to data mining and statistical pattern recognition. It should become a classic…It is especially good for statisticians interested in high-dimensional and high-volume data such as can be found in telephone records, satellite images, and genetic microarrays. It can be used for an advanced special topics course in statistics for graduate students."
TECHNOMETRICS
"[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features…As a source for the methods of statistical learning…it will probably be a long time before there is a competitor to this book."
SHORT BOOK REVIEWS
"This book describes modern tools for data analysis. With the exception of the last chapter, it is concerned with "supervised" methods - those methods in which a sample of cases is available, including values of an outcome variable, and on which one can build a model allowing one to predict the value of the outcome variable for new cases. The authors are amongst the leaders in this area, having developed many of the modern tools. Such methods have seen extraordinary development in recent decades, primarily because of progress in computer technology, but also because of the huge range of applications. Furthermore, the practical development of these modeling and inferential tools has resulted in a deeper theoretical understanding of the modeling process... The book includes many special cases and examples, which give insights into the ideas and methods. It explains very clearly the relationships between the methods, and covers both standard statistical staples, such as linear and logistic regression, as well as modern tools. It is not overburdened with unnecessary mathematics but uses only what is necessary for the practical application of the methods...The book has been beautifully produced. It was a pleasure to read. I strongly recommend it."
MATHEMATICAL REVIEWS
"The book provides a comprehensive and up-to-date introduction to the field of statistical pattern recognition, now commonly referred to as statistical learning…Browsing through the book, one is immediately attracted to the skillful use of color plots to stress the different behaviors of algorithms on real-world datasets. This tells a lot about the books style: intuition about a learning technique is built by looking at the behavior on the data, then the statistical analysis follows. However, even in its most technical parts, the presentation flows very smoothly, avoiding the definition-theorem-proof writing style…this is a very complete and up-to-date work covering all the most important learning techniques, which are presented in a rigorous but accessible statistical framework."
JOURNAL OF CLASSIFICATION, JUNE 2004
"This is a great book. All three authors have track records for clear exposition and are famously gifted for finding intuitive explanations that illuminate technical results…In particular, we admire the book for its:
-outstanding use of real data examples to motivate problems and methods;
-unified treatment of flexible inferential procedures in terms of maximization of an objective function subject to a complexity penalty;
-lucid explanation of the amazing performance of the AdaBoost algorithm in improving classification accuracy for almost any rule;
-clear account of support vector machines in terms of traditional statistical paradigms;
-regular introduction of some new insight, such as describing self-organizing maps as constrained k-means clustering.
…No modern statistician or computer scientist should be without this book."
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, JUNE 2004
"In the words of the authors, the goal of this book was to ‘bring together many of the important new ideas in learning, and explain them in a statistical framework.’ The authors have been quite successful in achieving this objective, and their work is a welcome addition to the statistics and learning literatures…A strength of the book is the attempt to organize a plethora of methods into a coherent whole. The relationships among the methods are emphasized. I know of no other book that covers so much ground."
"The book by Hastie et al. covers a wider number of topics such as supervised learning based on linear models, nearest neighbor methods, decision theory, function approximations, roughness, and kernels … . The charts and graphs are done in color to distinguish different patterns. The book has both challenging and easier exercise problems in each chapter. The book is suitable for a graduate level data mining course. I learned a lot from this well written book and recommend it highly." (Ramalingam Shanmugam, Journal of Statistical Computation & Simulation, Vol. 74 (4), 2004)
"One of the great features of the book is that it really contains more or less all modern methods for statistical learning, so it gives the reader a very good overview of this important field. … The author worked very hard on presentation of the material, in particular they illustrated the material by many colored graphics. … I think this book is valuable for anyone interested in statistical learning and its application, and I am happy to have it on my desk." (Michael Kohler, Metrika, February, 2003)
"For anyone who … wants to learn the new terminology and to understand what the ‘competition’ is doing, this is the book to buy. … the thinking is still very much statistical. This makes the book very easy to digest and pleasant to read for people with a statistical background. The many superb graphs add to this pleasure. … The book is important because it shows that interaction between statistics and machine learning can be profitable for both fields." (Hans C. van Houwelingen, Statistics in Medicine, Vol. 23, 2004)
"This is a great book. … We have taught a large graduate course (for statisticians and computer scientists) in data mining from this book. In developing this course we spoke to many other faculty members at a range of institutions, and we found no one who did not enjoy reading and teaching from this text. … there is no other book worth considering for such a course. … The book has beautiful graphics … . No modern statistician or computer scientist should be without this book." (David Banks and Feng Liang, Journal of Classification, Vol. 21 (1), 2004)
"The book provides a long-sought link between Statistics and Data Mining. It provides an excellent reference for researchers in information sciences … . Written by well-known specialists in applied statistics, the book provides a good practical orientation, with related theoretical issues coming out quite clearly. … this is the first book to address different aspects of data mining, inference and prediction in a coherently interdisciplinary context. … this book will always be remembered for laying the foundation of that scientific pyramid." (Kassim Said Mwitondi, Journal of Applied Statistics, Vol. 30 (1), 2003)
"The emphasis is on concepts rather than mathematics, and several examples are given as illustration. … This book is designed for researchers and students in a broad variety of fields such as statistics, artificial intelligence, engineering and finance. It should be a valuable resource for those who are interested in data mining in science or industry. I believe that it will be a very useful addition to any scholarly library." (Theofanis Sapatinas, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol. 157 (1), 2004)
"A mere glance at the table of contents gives an idea of the breadth and depth of coverage of this remarkable book. … The style of this beautifully presented book is friendly and intuitive, and at the same time clear and rigorous. All the techniques dealt with are presented and compared through examples with real and simulated data. … The book can be used as a basis for courses of different levels, from the purely practical to the thoroughly theoretical. … a wonderful book!" (Ricardo Maronna, Statistical Papers, Vol. 44 (3), 2003)
"The book covers two topics: 12 chapters discuss statistical methods of supervised learning, the final chapter is on unsupervised learning. … The getup of the book is outstanding … . The book is an excellent and comprehensive treatment of the topics for which the authors are well known … . The book may well serve as a textbook for an advanced course; the illustrating examples and the discussion of computational aspects make the book useful for those who want to apply the methods." (Peter Hackl, Statistical Papers, Vol. 43 (3), 2002)

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关键词：Statistical Data Mining statistica statistic Learning Edition The Mining Elements Learning

Product Description
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.
Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
FROM THE REVIEWS:
TECHNOMETRICS "[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features...As a source for the methods of statistical learning...it will probably be a long time before there is a competitor to this book."

Product Details Hardcover: 552 pages Publisher: Springer; Corrected edition (July 30, 2003) Language: English ISBN-10: 0387952845 ISBN-13: 978-0387952840

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 Di±culty of the Problem 32

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 Tradeo® 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

4 Linear Methods for Classi¯cation 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 Tradeo® 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

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 Classi¯cation 208

6.6.1 Kernel Density Estimation 208

6.6.2 Kernel Density Classi¯cation 210

6.6.3 The Naive Bayes Classi¯er 210

6.7 Radial Basis Functions and Kernels 212

6.8 Mixture Models for Density Estimation and Classi¯cation 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 Tradeo® 226

7.4 Optimism of the Training Error Rate 228

7.5 Estimates of In-Sample Prediction Error 230

7.6 The E®ective 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

8 Model Inference and Averaging 261

8.1 Introduction 261

8.2 The Bootstrap and Maximum Likelihood Methods 261

8.2.1 A Smoothing Example 261

8.2.2 Maximum Likelihood Inference 265

8.2.3 Bootstrap versus Maximum Likelihood 267

8.3 Bayesian Methods 267

8.4 Relationship Between the Bootstrap

and Bayesian Inference 271

8.5 The EM Algorithm 272

8.5.1 Two-Component Mixture Model 272

8.5.2 The EM Algorithm in General 276

8.5.3 EM as a Maximization{Maximization Procedure 277

8.6 MCMC for Sampling from the Posterior 279

8.7 Bagging 282

8.7.1 Example: Trees with Simulated Data 283

8.8 Model Averaging and Stacking 288

8.9 Stochastic Search: Bumping 290

Bibliographic Notes 292

Exercises 293

9 Additive Models, Trees, and Related Methods 295

9.1 Generalized Additive Models 295

9.1.1 Fitting Additive Models 297

9.1.2 Example: Additive Logistic Regression 299

9.1.3 Summary 304

9.2 Tree-Based Methods 305

9.2.1 Background 305

9.2.2 Regression Trees 307

9.2.3 Classi¯cation Trees 308

9.2.4 Other Issues 310

9.2.5 Spam Example (Continued) 313

9.3 PRIM: Bump Hunting 317

9.3.1 Spam Example (Continued) 320

9.4 MARS: Multivariate Adaptive Regression Splines 321

9.4.1 Spam Example (Continued) 326

9.4.2 Example (Simulated Data) 327

9.4.3 Other Issues 328

9.5 Hierarchical Mixtures of Experts 329

9.6 Missing Data 332

9.7 Computational Considerations 334

Bibliographic Notes 334

Exercises 335

10 Boosting and Additive Trees 337

10.1 Boosting Methods 337

10.1.1 Outline of This Chapter 340

10.2 Boosting Fits an Additive Model 341

10.3 Forward Stagewise Additive Modeling 342

10.4 Exponential Loss and AdaBoost 343

10.5 Why Exponential Loss? 345

10.6 Loss Functions and Robustness 346

10.7 \O®-the-Shelf" Procedures for Data Mining 350

10.8 Example: Spam Data 352

10.9 Boosting Trees 353

10.10 Numerical Optimization via Gradient Boosting 358

10.10.1 Steepest Descent 358

10.10.2 Gradient Boosting 359

10.10.3 Implementations of Gradient Boosting 360

10.11 Right-Sized Trees for Boosting 361

10.12 Regularization 364

10.12.1 Shrinkage 364

10.12.2 Subsampling 365

10.13 Interpretation 367

10.13.1 Relative Importance of Predictor Variables 367

10.13.2 Partial Dependence Plots 369

10.14 Illustrations 371

10.14.1 California Housing 371

10.14.2 New Zealand Fish 375

10.14.3 Demographics Data 379

Bibliographic Notes 380

Exercises 384

11 Neural Networks 389

11.1 Introduction 389

11.2 Projection Pursuit Regression 389

11.3 Neural Networks 392

11.4 Fitting Neural Networks 395

11.5 Some Issues in Training Neural Networks 397

11.5.1 Starting Values 397

11.5.2 Over¯tting 398

11.5.3 Scaling of the Inputs 398

11.5.4 Number of Hidden Units and Layers 400

11.5.5 Multiple Minima 400

11.6 Example: Simulated Data 401

11.7 Example: ZIP Code Data 404

11.8 Discussion 408

11.9 Bayesian Neural Nets and the NIPS 2003 Challenge 409

11.9.1 Bayes, Boosting and Bagging 410

11.9.2 Performance Comparisons 412

11.10 Computational Considerations 414

Bibliographic Notes 415

Exercises 415

12 Support Vector Machines and

Flexible Discriminants 417

12.1 Introduction 417

12.2 The Support Vector Classi¯er 417

12.2.1 Computing the Support Vector Classi¯er 420

12.2.2 Mixture Example (Continued) 421

12.3 Support Vector Machines and Kernels 423

12.3.1 Computing the SVM for Classi¯cation 423

12.3.2 The SVM as a Penalization Method 426

12.3.3 Function Estimation and Reproducing Kernels 428

12.3.4 SVMs and the Curse of Dimensionality 431

12.3.5 A Path Algorithm for the SVM Classi¯er 432

12.3.6 Support Vector Machines for Regression 434

12.3.7 Regression and Kernels 436

12.3.8 Discussion 438

12.4 Generalizing Linear Discriminant Analysis 438

12.5 Flexible Discriminant Analysis 440

12.5.1 Computing the FDA Estimates 444

12.6 Penalized Discriminant Analysis 446

12.7 Mixture Discriminant Analysis 449

12.7.1 Example: Waveform Data 451

Bibliographic Notes 455

Exercises 455

13 Prototype Methods and Nearest-Neighbors 459

13.1 Introduction 459

13.2 Prototype Methods 459

13.2.1 K-means Clustering 460

13.2.2 Learning Vector Quantization 462

13.2.3 Gaussian Mixtures 463

13.3 k-Nearest-Neighbor Classi¯ers 463

13.3.1 Example: A Comparative Study 468

13.3.2 Example: k-Nearest-Neighbors

and Image Scene Classi¯cation 470

13.3.3 Invariant Metrics and Tangent Distance 471

13.4 Adaptive Nearest-Neighbor Methods 475

13.4.1 Example 478

13.4.2 Global Dimension Reduction

for Nearest-Neighbors 479

13.5 Computational Considerations 480

Bibliographic Notes 481

Exercises 481

14 Unsupervised Learning 485

14.1 Introduction 485

14.2 Association Rules 487

14.2.1 Market Basket Analysis 488

14.2.2 The Apriori Algorithm 489

14.2.3 Example: Market Basket Analysis 492

14.2.4 Unsupervised as Supervised Learning 495

14.2.5 Generalized Association Rules 497

14.2.6 Choice of Supervised Learning Method 499

14.2.7 Example: Market Basket Analysis (Continued) 499

14.3 Cluster Analysis 501

14.3.1 Proximity Matrices 503

14.3.2 Dissimilarities Based on Attributes 503

14.3.3 Object Dissimilarity 505

14.3.4 Clustering Algorithms 507

14.3.5 Combinatorial Algorithms 507

14.3.6 K-means 509

14.3.7 Gaussian Mixtures as Soft K-means Clustering 510

14.3.8 Example: Human Tumor Microarray Data 512

14.3.9 Vector Quantization 514

14.3.10 K-medoids 515

14.3.11 Practical Issues 518

14.3.12 Hierarchical Clustering 520

14.4 Self-Organizing Maps 528

14.5 Principal Components, Curves and Surfaces 534

14.5.1 Principal Components 534

14.5.2 Principal Curves and Surfaces 541

14.5.3 Spectral Clustering 544

14.5.4 Kernel Principal Components 547

14.5.5 Sparse Principal Components 550

14.6 Non-negative Matrix Factorization 553

14.6.1 Archetypal Analysis 554

14.7 Independent Component Analysis

and Exploratory Projection Pursuit 557

14.7.1 Latent Variables and Factor Analysis 558

14.7.2 Independent Component Analysis 560

14.7.3 Exploratory Projection Pursuit 565

14.7.4 A Direct Approach to ICA 565

14.8 Multidimensional Scaling 570

14.9 Nonlinear Dimension Reduction

and Local Multidimensional Scaling 572

14.10 The Google PageRank Algorithm 576

Bibliographic Notes 578

Exercises 579

15 Random Forests 587

15.1 Introduction 587

15.2 De¯nition of Random Forests 587

15.3 Details of Random Forests 592

15.3.1 Out of Bag Samples 592

15.3.2 Variable Importance 593

15.3.3 Proximity Plots 595

15.3.4 Random Forests and Over¯tting 596

15.4 Analysis of Random Forests 597

15.4.1 Variance and the De-Correlation E®ect 597

15.4.2 Bias 600

15.4.3 Adaptive Nearest Neighbors 601

Bibliographic Notes 602

Exercises 603

16 Ensemble Learning 605

16.1 Introduction 605

16.2 Boosting and Regularization Paths 607

16.2.1 Penalized Regression 607

16.2.2 The \Bet on Sparsity" Principle 610

16.2.3 Regularization Paths, Over-¯tting and Margins 613

16.3 Learning Ensembles 616

16.3.1 Learning a Good Ensemble 617

16.3.2 Rule Ensembles 622

Bibliographic Notes 623

Exercises 624

17 Undirected Graphical Models 625

17.1 Introduction 625

17.2 Markov Graphs and Their Properties 627

17.3 Undirected Graphical Models for Continuous Variables 630

17.3.1 Estimation of the Parameters

when the Graph Structure is Known 631

17.3.2 Estimation of the Graph Structure 635

17.4 Undirected Graphical Models for Discrete Variables 638

17.4.1 Estimation of the Parameters

when the Graph Structure is Known 639

17.4.2 Hidden Nodes 641

17.4.3 Estimation of the Graph Structure 642

17.4.4 Restricted Boltzmann Machines 643

Exercises 645