出版社: Springer
副标题: with Applications in R
出版年: 2013-8-12
页数: 426
定价: USD 79.99
装帧: Hardcover
丛书: Springer Texts in Statistics
ISBN: 9781461471370
内容简介 · · · · · ·
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, a...
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
An Introduction to Statistical Learning的创作者
· · · · · ·
作者简介 · · · · · ·
Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is an assoc...
Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/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.
目录 · · · · · ·
1 Introduction 1
2 Statistical Learning 15
2.1 What Is Statistical Learning? . . . . . . . . . . . . . . . . . 15
2.1.1 Why Estimate f? . . . . . . . . . . . . . . . . . . . . 17
2.1.2 How Do We Estimate f? . . . . . . . . . . . . . . . 21
· · · · · · (更多)
1 Introduction 1
2 Statistical Learning 15
2.1 What Is Statistical Learning? . . . . . . . . . . . . . . . . . 15
2.1.1 Why Estimate f? . . . . . . . . . . . . . . . . . . . . 17
2.1.2 How Do We Estimate f? . . . . . . . . . . . . . . . 21
2.1.3 The Trade-Off Between Prediction Accuracy
and Model Interpretability . . . . . . . . . . . . . . 24
2.1.4 Supervised Versus Unsupervised Learning . . . . . . 26
2.1.5 Regression Versus Classification Problems . . . . . . 28
2.2 Assessing Model Accuracy . . . . . . . . . . . . . . . . . . . 29
2.2.1 Measuring the Quality of Fit . . . . . . . . . . . . . 29
2.2.2 The Bias-Variance Trade-Off . . . . . . . . . . . . . 33
2.2.3 The Classification Setting . . . . . . . . . . . . . . . 37
2.3 Lab: Introduction to R . . . . . . . . . . . . . . . . . . . . . 42
2.3.1 Basic Commands . . . . . . . . . . . . . . . . . . . . 42
2.3.2 Graphics . . . . . . . . . . . . . . . . . . . . . . . . 45
2.3.3 Indexing Data . . . . . . . . . . . . . . . . . . . . . 47
2.3.4 Loading Data . . . . . . . . . . . . . . . . . . . . . . 48
2.3.5 Additional Graphical and Numerical Summaries . . 49
2.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Linear Regression 59
3.1 Simple Linear Regression . . . . . . . . . . . . . . . . . . . 61
3.1.1 Estimating the Coefficients . . . . . . . . . . . . . . 61
3.1.2 Assessing the Accuracy of the Coefficient
Estimates . . . . . . . . . . . . . . . . . . . . . . . . 63
3.1.3 Assessing the Accuracy of the Model . . . . . . . . . 68
3.2 Multiple Linear Regression . . . . . . . . . . . . . . . . . . 71
3.2.1 Estimating the Regression Coefficients . . . . . . . . 72
3.2.2 Some Important Questions . . . . . . . . . . . . . . 75
3.3 Other Considerations in the Regression Model . . . . . . . . 82
3.3.1 Qualitative Predictors . . . . . . . . . . . . . . . . . 82
3.3.2 Extensions of the Linear Model . . . . . . . . . . . . 86
3.3.3 Potential Problems . . . . . . . . . . . . . . . . . . . 92
3.4 The Marketing Plan . . . . . . . . . . . . . . . . . . . . . . 102
3.5 Comparison of Linear Regression with K-Nearest
Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.6 Lab: Linear Regression . . . . . . . . . . . . . . . . . . . . . 109
3.6.1 Libraries . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.6.2 Simple Linear Regression . . . . . . . . . . . . . . . 110
3.6.3 Multiple Linear Regression . . . . . . . . . . . . . . 113
3.6.4 Interaction Terms . . . . . . . . . . . . . . . . . . . 115
3.6.5 Non-linear Transformations of the Predictors . . . . 115
3.6.6 Qualitative Predictors . . . . . . . . . . . . . . . . . 117
3.6.7 Writing Functions . . . . . . . . . . . . . . . . . . . 119
3.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4 Classification 127
4.1 An Overview of Classification . . . . . . . . . . . . . . . . . 128
4.2 Why Not Linear Regression? . . . . . . . . . . . . . . . . . 129
4.3 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . 130
4.3.1 The Logistic Model . . . . . . . . . . . . . . . . . . . 131
4.3.2 Estimating the Regression Coefficients . . . . . . . . 133
4.3.3 Making Predictions . . . . . . . . . . . . . . . . . . . 134
4.3.4 Multiple Logistic Regression . . . . . . . . . . . . . . 135
4.3.5 Logistic Regression for >2 Response Classes . . . . . 137
4.4 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . 138
4.4.1 Using Bayes’ Theorem for Classification . . . . . . . 138
4.4.2 Linear Discriminant Analysis for p = 1 . . . . . . . . 139
4.4.3 Linear Discriminant Analysis for p >1 . . . . . . . . 142
4.4.4 Quadratic Discriminant Analysis . . . . . . . . . . . 149
4.5 A Comparison of Classification Methods . . . . . . . . . . . 151
4.6 Lab: Logistic Regression, LDA, QDA, and KNN . . . . . . 154
4.6.1 The Stock Market Data . . . . . . . . . . . . . . . . 154
4.6.2 Logistic Regression . . . . . . . . . . . . . . . . . . . 156
4.6.3 Linear Discriminant Analysis . . . . . . . . . . . . . 161
4.6.4 Quadratic Discriminant Analysis . . . . . . . . . . . 163
4.6.5 K-Nearest Neighbors . . . . . . . . . . . . . . . . . . 163
4.6.6 An Application to Caravan Insurance Data . . . . . 165
4.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
5 Resampling Methods 175
5.1 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . 176
5.1.1 The Validation Set Approach . . . . . . . . . . . . . 176
5.1.2 Leave-One-Out Cross-Validation . . . . . . . . . . . 178
5.1.3 k-Fold Cross-Validation . . . . . . . . . . . . . . . . 181
5.1.4 Bias-Variance Trade-Off for k-Fold
Cross-Validation . . . . . . . . . . . . . . . . . . . . 183
5.1.5 Cross-Validation on Classification Problems . . . . . 184
5.2 The Bootstrap . . . . . . . . . . . . . . . . . . . . . . . . . 187
5.3 Lab: Cross-Validation and the Bootstrap . . . . . . . . . . . 190
5.3.1 The Validation Set Approach . . . . . . . . . . . . . 191
5.3.2 Leave-One-Out Cross-Validation . . . . . . . . . . . 192
5.3.3 k-Fold Cross-Validation . . . . . . . . . . . . . . . . 193
5.3.4 The Bootstrap . . . . . . . . . . . . . . . . . . . . . 194
5.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
6 Linear Model Selection and Regularization 203
6.1 Subset Selection . . . . . . . . . . . . . . . . . . . . . . . . 205
6.1.1 Best Subset Selection . . . . . . . . . . . . . . . . . 205
6.1.2 Stepwise Selection . . . . . . . . . . . . . . . . . . . 207
6.1.3 Choosing the Optimal Model . . . . . . . . . . . . . 210
6.2 Shrinkage Methods . . . . . . . . . . . . . . . . . . . . . . . 214
6.2.1 Ridge Regression . . . . . . . . . . . . . . . . . . . . 215
6.2.2 The Lasso . . . . . . . . . . . . . . . . . . . . . . . . 219
6.2.3 Selecting the Tuning Parameter . . . . . . . . . . . . 227
6.3 Dimension Reduction Methods . . . . . . . . . . . . . . . . 228
6.3.1 Principal Components Regression . . . . . . . . . . . 230
6.3.2 Partial Least Squares . . . . . . . . . . . . . . . . . 237
6.4 Considerations in High Dimensions . . . . . . . . . . . . . . 238
6.4.1 High-Dimensional Data . . . . . . . . . . . . . . . . 238
6.4.2 What Goes Wrong in High Dimensions? . . . . . . . 239
6.4.3 Regression in High Dimensions . . . . . . . . . . . . 241
6.4.4 Interpreting Results in High Dimensions . . . . . . . 243
6.5 Lab 1: Subset Selection Methods . . . . . . . . . . . . . . . 244
6.5.1 Best Subset Selection . . . . . . . . . . . . . . . . . 244
6.5.2 Forward and Backward Stepwise Selection . . . . . . 247
6.5.3 Choosing Among Models Using the Validation
Set Approach and Cross-Validation . . . . . . . . . . 248
6.6 Lab 2: Ridge Regression and the Lasso . . . . . . . . . . . . 251
6.6.1 Ridge Regression . . . . . . . . . . . . . . . . . . . . 251
6.6.2 The Lasso . . . . . . . . . . . . . . . . . . . . . . . . 255
6.7 Lab 3: PCR and PLS Regression . . . . . . . . . . . . . . . 256
6.7.1 Principal Components Regression . . . . . . . . . . . 256
6.7.2 Partial Least Squares . . . . . . . . . . . . . . . . . 258
6.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
7 Moving Beyond Linearity 265
7.1 Polynomial Regression . . . . . . . . . . . . . . . . . . . . . 266
7.2 Step Functions . . . . . . . . . . . . . . . . . . . . . . . . . 268
7.3 Basis Functions . . . . . . . . . . . . . . . . . . . . . . . . . 270
7.4 Regression Splines . . . . . . . . . . . . . . . . . . . . . . . 271
7.4.1 Piecewise Polynomials . . . . . . . . . . . . . . . . . 271
7.4.2 Constraints and Splines . . . . . . . . . . . . . . . . 271
7.4.3 The Spline Basis Representation . . . . . . . . . . . 273
7.4.4 Choosing the Number and Locations
of the Knots . . . . . . . . . . . . . . . . . . . . . . 274
7.4.5 Comparison to Polynomial Regression . . . . . . . . 276
7.5 Smoothing Splines . . . . . . . . . . . . . . . . . . . . . . . 277
7.5.1 An Overview of Smoothing Splines . . . . . . . . . . 277
7.5.2 Choosing the Smoothing Parameter λ . . . . . . . . 278
7.6 Local Regression . . . . . . . . . . . . . . . . . . . . . . . . 280
7.7 Generalized Additive Models . . . . . . . . . . . . . . . . . 282
7.7.1 GAMs for Regression Problems . . . . . . . . . . . . 283
7.7.2 GAMs for Classification Problems . . . . . . . . . . 286
7.8 Lab: Non-linear Modeling . . . . . . . . . . . . . . . . . . . 287
7.8.1 Polynomial Regression and Step Functions . . . . . 288
7.8.2 Splines . . . . . . . . . . . . . . . . . . . . . . . . . . 293
7.8.3 GAMs . . . . . . . . . . . . . . . . . . . . . . . . . . 294
7.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
8 Tree-Based Methods 303
8.1 The Basics of Decision Trees . . . . . . . . . . . . . . . . . 303
8.1.1 Regression Trees . . . . . . . . . . . . . . . . . . . . 304
8.1.2 Classification Trees . . . . . . . . . . . . . . . . . . . 311
8.1.3 Trees Versus Linear Models . . . . . . . . . . . . . . 314
8.1.4 Advantages and Disadvantages of Trees . . . . . . . 315
8.2 Bagging, Random Forests, Boosting . . . . . . . . . . . . . 316
8.2.1 Bagging . . . . . . . . . . . . . . . . . . . . . . . . . 316
8.2.2 Random Forests . . . . . . . . . . . . . . . . . . . . 319
8.2.3 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . 321
8.3 Lab: Decision Trees . . . . . . . . . . . . . . . . . . . . . . . 323
8.3.1 Fitting Classification Trees . . . . . . . . . . . . . . 323
8.3.2 Fitting Regression Trees . . . . . . . . . . . . . . . . 327
8.3.3 Bagging and Random Forests . . . . . . . . . . . . . 328
8.3.4 Boosting . . . . . . . . . . . . . . . . . . . . . . . . . 330
8.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
9 Support Vector Machines 337
9.1 Maximal Margin Classifier . . . . . . . . . . . . . . . . . . . 338
9.1.1 What Is a Hyperplane? . . . . . . . . . . . . . . . . 338
9.1.2 Classification Using a Separating Hyperplane . . . . 339
9.1.3 The Maximal Margin Classifier . . . . . . . . . . . . 341
9.1.4 Construction of the Maximal Margin Classifier . . . 342
9.1.5 The Non-separable Case . . . . . . . . . . . . . . . . 343
9.2 Support Vector Classifiers . . . . . . . . . . . . . . . . . . . 344
9.2.1 Overview of the Support Vector Classifier . . . . . . 344
9.2.2 Details of the Support Vector Classifier . . . . . . . 345
9.3 Support Vector Machines . . . . . . . . . . . . . . . . . . . 349
9.3.1 Classification with Non-linear Decision
Boundaries . . . . . . . . . . . . . . . . . . . . . . . 349
9.3.2 The Support Vector Machine . . . . . . . . . . . . . 350
9.3.3 An Application to the Heart Disease Data . . . . . . 354
9.4 SVMs with More than Two Classes . . . . . . . . . . . . . . 355
9.4.1 One-Versus-One Classification . . . . . . . . . . . . . 355
9.4.2 One-Versus-All Classification . . . . . . . . . . . . . 356
9.5 Relationship to Logistic Regression . . . . . . . . . . . . . . 356
9.6 Lab: Support Vector Machines . . . . . . . . . . . . . . . . 359
9.6.1 Support Vector Classifier . . . . . . . . . . . . . . . 359
9.6.2 Support Vector Machine . . . . . . . . . . . . . . . . 363
9.6.3 ROC Curves . . . . . . . . . . . . . . . . . . . . . . 365
9.6.4 SVM with Multiple Classes . . . . . . . . . . . . . . 366
9.6.5 Application to Gene Expression Data . . . . . . . . 366
9.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368
10 Unsupervised Learning 373
10.1 The Challenge of Unsupervised Learning . . . . . . . . . . . 373
10.2 Principal Components Analysis . . . . . . . . . . . . . . . . 374
10.2.1 What Are Principal Components? . . . . . . . . . . 375
10.2.2 Another Interpretation of Principal Components . . 379
10.2.3 More on PCA . . . . . . . . . . . . . . . . . . . . . . 380
10.2.4 Other Uses for Principal Components . . . . . . . . 385
10.3 Clustering Methods . . . . . . . . . . . . . . . . . . . . . . . 385
10.3.1 K-Means Clustering . . . . . . . . . . . . . . . . . . 386
10.3.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . 390
10.3.3 Practical Issues in Clustering . . . . . . . . . . . . . 399
10.4 Lab 1: Principal Components Analysis . . . . . . . . . . . . 401
10.5 Lab 2: Clustering . . . . . . . . . . . . . . . . . . . . . . . . 404
10.5.1 K-Means Clustering . . . . . . . . . . . . . . . . . . 404
10.5.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . 406
10.6 Lab 3: NCI60 Data Example . . . . . . . . . . . . . . . . . 407
10.6.1 PCA on the NCI60 Data . . . . . . . . . . . . . . . 408
10.6.2 Clustering the Observations of the NCI60 Data . . . 410
10.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
Index 419
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An Introduction to Statistical Learning的书评 · · · · · · ( 全部 39 条 )
这是一本很好的机器学习的入门书(最后附上书和课后习题解答的英文版)
烂到天地惊:王星《统计学习导论》翻译恶搞记
王星团队=盲人组合?王星《统计学习导论》翻译批评
关于ISL的一些资料
> 更多书评 39篇
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在这本书的论坛里发言这本书的其他版本 · · · · · · ( 全部6 )
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机械工业出版社 (2015)8.7分 166人读过
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Springer (2021)9.6分 22人读过
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Springer (2023)暂无评分 8人读过
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订阅关于An Introduction to Statistical Learning的评论:
feed: rss 2.0
16 有用 阿道克 2013-07-23 22:32:29
果然是element of statistical learning的R语言简明版。或者看成ESL的导读也行。
2 有用 茼蒿 2017-03-31 08:39:21
理论解释非常到位,但需要结合code与case study来消化吸收,应用
7 有用 薛定谔的猫 2015-12-06 06:23:36
statistical learning的入门级教材,不需要很多的数学,但涵盖了许多topic,而且每章结尾都有R的实例,不过这本书还是过于基础,unsupervised learning只有一章,而且居然跳过了neural networks。
3 有用 蓝葻 2013-12-12 16:21:52
Beyond the scope of this book...有的内容不讲原理给再多例子也没意思
22 有用 caibinbupt 2014-03-28 23:30:39
公开课的教材,没涉及太多的数学,不错。https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/courseware/dfece96897994039a17547b575573447/
0 有用 🔭 2024-02-15 07:16:16 美国
这本书太适合统计入门和基础概念理解了!Hypothesis testing这部分完全弥补了我的困惑🤧
0 有用 Akizumi 2023-12-13 22:15:30 北京
大概以后搞机器学习就是源于这本书吧
0 有用 momo 2023-10-27 05:41:12 新加坡
应付下本科实习/fresh grad的面试,蛮可以的🤭 就省去了去互联网上找“说人话”的资料的时间🤭
0 有用 捡面包屑鼠仔 2023-07-07 16:11:35 陕西
先会用,再深挖 2nd book in Stanford I love stanford!!!!我爱这两个老头
1 有用 Foodsgerald 2023-06-28 14:10:47 美国
所谓对一个领域致知,不过是愿意去细致地消化其中教科书两三本、两三遍而已。真是相见恨晚