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Index 425 read.table(), 48, 49 regsubsets(), 244–249, 262 residuals(), 112 return(), 172 rm(), 43 rnorm(), 44, 45, 124, 262, 417 rstudent(), 112 runif(), 417 s(), 294 sample(), 191, 194, 414 savehistory(), 51 scale(), 165, 406, 417 sd(), 45 seq(), 46 set.seed(), 45, 191, 405 smooth.spline(), 293, 294 sqrt(), 44, 45 sum(), 244 summary(), 51, 55, 113, 121, 122, 157, 196, 199, 244, 245, 256, 257, 295, 324, 325, 328, 330, 334, 360, 361, 363, 372, 408 svm(), 359–363, 365, 366 table(), 158, 417 text(), 325 title(), 289 tree(), 304, 324 tune(), 361, 364, 372 update(), 114 var(), 45 varImpPlot(), 330 vif(), 114 which.max(), 113, 246 which.min(), 246 write.table(), 48 radial kernel, 352–354, 363 random forest, 12, 303, 316, 320–321, 328–330 recall, 147 receiver operating characteristic (ROC), 147, 354–355 recursive binary splitting, 306, 309, 311 reducible error, 18, 81 regression, 3, 12, 28–29 local, 265, 266, 280–282 piecewise polynomial, 271 polynomial, 265–268, 276–277 spline, 266, 270, 293 tree, 304–311, 327–328 regularization, 204, 215 replacement, 189 resampling, 175–190 residual, 62, 72 plot, 92 standard error, 66, 68–69, 79–80, 102 studentized, 97 sum of squares, 62, 70, 72 residuals, 239, 322 response, 15 ridge regression, 12, 215–219, 357 robust, 345, 348, 400 ROC curve, 147, 354–355 rug plot, 292 scale invariant, 217 scatterplot, 49 scatterplot matrix, 50 scree plot, 383–384, 409 elbow, 384 seed, 191 semi-supervised learning, 28 sensitivity, 145, 147 separating hyperplane, 338–343 shrinkage, 204, 215 penalty, 215 signal, 228 slack variable, 346 slope, 61, 63 Smarket data set, 3, 14, 154, 161, 162, 171 smoother, 286 smoothing spline, 266, 277–280, 293 soft margin classifier, 343–345
426 Index soft-thresholding, 225 sparse, 219, 228 sparsity, 219 specificity, 145, 147, 148 spline, 265, 271–280 cubic, 273 linear, 273 natural, 274, 278 regression, 266, 271–277 smoothing, 31, 266, 277–280 thin-plate, 23 standard error, 65, 93 standardize, 165 statistical model, 1 step function, 105, 265, 268–270 stepwise model selection, 12, 205, 207 stump, 323 subset selection, 204–214 subtree, 308 supervised learning, 26–28, 237 support vector, 342, 347, 357 classifier, 337, 343–349 machine, 12, 26, 349–359 regression, 358 synergy, 60, 81, 87–90, 104 systematic, 16 t-distribution, 67, 153 t-statistic, 67 test error, 37, 40, 158 MSE, 29–34 observations, 30 set, 32 time series, 94 total sum of squares, 70 tracking, 94 train, 21 training data, 21 error, 37, 40, 158 MSE, 29–33 tree, 303–316 tree-based method, 303 true negative, 147 true positive, 147 true positive rate, 147, 149, 354 truncated power basis, 273 tuning parameter, 215 Type I error, 147 Type II error, 147 unsupervised learning, 26–28, 230, 237, 373–413 USArrests data set, 14, 377, 378, 381–383 validation set, 176 approach, 176–178 variable, 15 dependent, 15 dummy, 82–86, 89–90 importance, 319, 330 independent, 15 indicator, 37 input, 15 output, 15 qualitative, 82–86, 89–90 selection, 78, 204, 219 variance, 19, 33–36 inflation factor, 101–103, 114 varying coefficient model, 282 vector, 43 Wage data set, 1, 2, 9, 10, 14, 267, 269, 271, 272, 274–277, 280, 281, 283, 284, 286, 287, 299 weakest link pruning, 308 Weekly data set, 14, 171, 200 weighted least squares, 96, 282 within class covariance, 143 workspace, 51 wrapper, 289
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Springer Texts in Statistics Gareth
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Gareth James • Daniela Witten •
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To our parents: Alison and Michael
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viii Preface disciplines who wish t
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x Contents 3 Linear Regression 59 3
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xii Contents 6.6 Lab 2: Ridge Regre
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xiv Contents 10.5 Lab 2: Clustering
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2 1. Introduction Wage 50 100 200 3
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4 1. Introduction Predicted Probabi
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6 1. Introduction of least squares,
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8 1. Introduction in order to contr
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10 1. Introduction In general, we w
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12 1. Introduction of A and B is de
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14 1. Introduction Name Auto Boston
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16 2. Statistical Learning Sales 5
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18 2. Statistical Learning Income Y
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20 2. Statistical Learning In this
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26 2. Statistical Learning additive
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28 2. Statistical Learning tistical
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30 2. Statistical Learning where ˆ
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32 2. Statistical Learning We now m
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34 2. Statistical Learning Y −10
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36 2. Statistical Learning 0.0 0.5
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38 2. Statistical Learning o o o o
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40 2. Statistical Learning o o o o
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42 2. Statistical Learning Error Ra
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44 2. Statistical Learning The matr
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46 2. Statistical Learning We will
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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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Page 436 and 437: Index 421 Wage, 1, 2, 9, 10, 14, 26
Page 438 and 439: Index 423 noise, 22, 228 non-linear
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