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Preface Statistical learning refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. The field encompasses many methods such as the lasso and sparse regression, classification and regression trees, and boosting and support vector machines. With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas as well as marketing, finance, and other business disciplines. People with statistical learning skills are in high demand. One of the first books in this area—The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statistics but also in related fields. One of the reasons for ESL’s popularity is its relatively accessible style. But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the applications of the methods and less on the mathematical details. We have created labs illustrating how to implement each of the statistical learning methods using the popular statistical software package R. These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master’s students in statistics or related quantitative fields or for individuals in other vii
viii Preface disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning one or two semesters. We would like to thank several readers for valuable comments on preliminary drafts of this book: Pallavi Basu, Alexandra Chouldechova, Patrick Danaher, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Courtney Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, and Xin Lu Tan. It’s tough to make predictions, especially about the future. -Yogi Berra Los Angeles, USA Seattle, USA Palo Alto, USA Palo Alto, USA Gareth James Daniela Witten Trevor Hastie Robert Tibshirani
Page 1 and 2: Springer Texts in Statistics Gareth
Page 4 and 5: Gareth James • Daniela Witten •
Page 6: To our parents: Alison and Michael
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44 2. Statistical Learning The matr
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48 2. Statistical Learning [3,] 3 7
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50 2. Statistical Learning > plot(c
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52 2. Statistical Learning 2.4 Exer
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54 2. Statistical Learning (b) What
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56 2. Statistical Learning 9. This
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3 Linear Regression This chapter is
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3.1 Simple Linear Regression 3.1 Si
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3.1 Simple Linear Regression 63 3 2
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3.1 Simple Linear Regression 65 con
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3.1 Simple Linear Regression 67 In
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Quantity Value Residual standard er
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3.2 Multiple Linear Regression 71 a
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3.2 Multiple Linear Regression 73 Y
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3.2 Multiple Linear Regression 75 T
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3.2 Multiple Linear Regression 77 w
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3.2 Multiple Linear Regression 79 i
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3.2 Multiple Linear Regression 81 S
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3.3 Other Considerations in the Reg
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x i = 3.3 Other Considerations in t
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3.4 The Marketing Plan 103 units wh
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y x 2 y x 2 3.5 Comparison of Linea
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3.5 Comparison of Linear Regression
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3.6 Lab: Linear Regression 109 p=1
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3.6 Lab: Linear Regression 111 Coef
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3.6 Lab: Linear Regression 113 > pl
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3.6 Lab: Linear Regression 115 > lm
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3.6 Lab: Linear Regression 117 > lm
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3.6 Lab: Linear Regression 119 Shel
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3.7 Exercises 121 (b) Answer (a) us
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3.7 Exercises 123 10. This question
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3.7 Exercises 125 (d) Create a scat
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4 Classification The linear regress
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4.2 Why Not Linear Regression? 129
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4.3 Logistic Regression 131 0 500 1
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4.3 Logistic Regression 133 β 1 do
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4.3 Logistic Regression 135 Coeffic
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4.3 Logistic Regression 137 Default
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4.4 Linear Discriminant Analysis 13
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4.5 A Comparison of Classification
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4.5 A Comparison of Classification
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4.6 Lab: Logistic Regression, LDA,
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4.7 Exercises 169 we will use obser
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Applied 4.7 Exercises 171 10. This
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4.7 Exercises 173 return(result) Th
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176 5. Resampling Methods 5.1 Cross
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178 5. Resampling Methods Mean Squa
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180 5. Resampling Methods LOOCV 10
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182 5. Resampling Methods Mean Squa
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184 5. Resampling Methods has highe
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186 5. Resampling Methods Error Rat
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188 5. Resampling Methods Y −2
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190 5. Resampling Methods Obs X Y Z
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192 5. Resampling Methods > mean((m
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194 5. Resampling Methods first is
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196 5. Resampling Methods Next, we
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198 5. Resampling Methods (f) When
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200 5. Resampling Methods predict.g
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6 Linear Model Selection and Regula
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6.1 Subset Selection 205 6.1 Subset
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6.1 Subset Selection 207 p = 20, th
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6.1 Subset Selection 209 #Variables
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6.1 Subset Selection 211 C p 10000
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6.1 Subset Selection 213 will lead
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6.2 Shrinkage Methods 215 obvious w
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6.2 Shrinkage Methods 217 regressio
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6.2 Shrinkage Methods 219 discussed
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6.2 Shrinkage Methods 221 respectiv
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6.2 Shrinkage Methods 223 Mean Squa
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6.2 Shrinkage Methods 225 To simpli
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6.2 Shrinkage Methods 227 (β j ) 0
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6.3 Dimension Reduction Methods 229
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6.4 Considerations in High Dimensio
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6.5 Lab 1: Subset Selection Methods
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6.6 Lab 2: Ridge Regression and the
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6.7 Lab 3: PCR and PLS Regression 2
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6.8 Exercises 259 final seven-compo
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6.8 Exercises 261 (a) As we increas
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6.8 Exercises 263 (d) Repeat (c), u
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7 Moving Beyond Linearity So far in
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|| 7.1 Polynomial Regression 267 De
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|| 7.2 Step Functions 269 Piecewise
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7.4 Regression Splines 271 7.4 Regr
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7.4 Regression Splines 273 plot is
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7.4 Regression Splines 275 Natural
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7.5 Smoothing Splines 277 Wage 50 1
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7.5 Smoothing Splines 279 to the nu
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7.6 Local Regression 281 Local Regr
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7.7 Generalized Additive Models 283
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7.7 Generalized Additive Models 285
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7.8 Lab: Non-linear Modeling 287 25
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7.8 Lab: Non-linear Modeling 289 po
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7.8 Lab: Non-linear Modeling 291 po
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7.8.2 Splines 7.8 Lab: Non-linear M
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7.8 Lab: Non-linear Modeling 295 In
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7.9 Exercises 297 It is easy to see
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7.9 Exercises 299 5. Consider two c
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7.9 Exercises 301 (a) Generate a re
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304 8. Tree-Based Methods Years < 4
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306 8. Tree-Based Methods have been
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308 8. Tree-Based Methods R5 R2 t4
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310 8. Tree-Based Methods Years < 4
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312 8. Tree-Based Methods E =1− m
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314 8. Tree-Based Methods Figure 8.
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316 8. Tree-Based Methods ▼ Unfor
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318 8. Tree-Based Methods Error 0.1
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320 8. Tree-Based Methods 8.2.2 Ran
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322 8. Tree-Based Methods of the ot
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324 8. Tree-Based Methods stumps wi
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326 8. Tree-Based Methods > tree.ca
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328 8. Tree-Based Methods Residual
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330 8. Tree-Based Methods The test
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332 8. Tree-Based Methods 8.4 Exerc
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334 8. Tree-Based Methods (e) Use r
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9 Support Vector Machines In this c
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9.1 Maximal Margin Classifier 339 X
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9.1 Maximal Margin Classifier 341 h
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9.1 Maximal Margin Classifier 343 m
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9.2 Support Vector Classifiers 345
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9.2 Support Vector Classifiers 347
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9.3 Support Vector Machines 349 X2
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9.3 Support Vector Machines 351 in
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9.3 Support Vector Machines 353 X2
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9.4 SVMs with More than Two Classes
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9.5 Relationship to Logistic Regres
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9.6 Lab: Support Vector Machines 35
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9.6.3 ROC Curves 9.6 Lab: Support V
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Obs. X 1 X 2 Y 1 3 4 Red 2 2 2 Red
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9.7 Exercises 371 (b) Compute the c
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10 Unsupervised Learning Most of th
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10.2 Principal Components Analysis
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10.2.4 Other Uses for Principal Com
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10.3 Clustering Methods 387 K=2 K=3
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10.3 Clustering Methods 389 Data St
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10.3 Clustering Methods 391 X2 −2
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10.3 Clustering Methods 393 0.0 0.5
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Algorithm 10.2 Hierarchical Cluster
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10.3 Clustering Methods 397 Average
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10.3 Clustering Methods 399 0 2 4 6
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10.4 Lab 1: Principal Components An
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10.4 Lab 1: Principal Components An
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10.5 Lab 2: Clustering 405 Cluster
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10.6 Lab 3: NCI60 Data Example 407
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10.7 Exercises 413 differ: for inst
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10.7 Exercises 415 (b) At a certain
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10.7 Exercises 417 10. In this prob
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Index C p , 78, 205, 206, 210-213 R
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Index 421 Wage, 1, 2, 9, 10, 14, 26
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Index 423 noise, 22, 228 non-linear
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Index 425 read.table(), 48, 49 regs
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