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Index 423 noise, 22, 228 non-linear, 2, 12, 265–301 decision boundary, 349–353 kernel, 349–353 non-parametric, 21, 23–24, 104–109, 168 normal (Gaussian) distribution, 138, 139, 142–143 null, 145 hypothesis, 67 model, 78, 205, 220 odds, 132, 170 OJ data set, 14, 334, 371 one-standard-error rule, 214 one-versus-all, 356 one-versus-one, 355 optimal separating hyperplane, 341 optimism of training error, 32 ordered categorical variable, 292 orthogonal, 233, 377 basis, 288 out-of-bag, 317–318 outlier, 96–97 output variable, 15 overfitting, 22, 24, 26, 32, 80, 144, 207, 341 p-value, 67–68, 73 parameter, 61 parametric, 21–23, 104–109 partial least squares, 12, 230, 237–238, 258, 259 path algorithm, 224 perpendicular, 233 polynomial kernel, 352, 354 regression, 90–92, 265–268, 271 population regression line, 63 Portfolio data set, 14, 194 positive predictive value, 147, 149 posterior distribution, 226 mode, 226 probability, 139 power, 101, 147 precision, 147 prediction, 17 interval, 82, 103 predictor, 15 principal components, 375 analysis, 12, 230–236, 374–385 loading vector, 375, 376 proportion of variance explained, 382–384, 408 regression, 12, 230–236, 256–257, 374–375, 385 score vector, 376 scree plot, 383–384 prior distribution, 226 probability, 138 projection, 204 pruning, 307–309 cost complexity, 307–309 weakest link, 307–309 quadratic, 91 quadratic discriminant analysis, 4, 149–150 qualitative, 3, 28, 127, 176 variable, 82–86 quantitative, 3, 28, 127, 176 R functions x 2 , 125 abline(), 112, 122, 301, 412 anova(), 116, 290, 291 apply(), 250, 401 as.dist(), 407 as.factor(), 50 attach(), 50 biplot(), 403 boot(), 194–196, 199
424 Index bs(), 293, 300 c(), 43 cbind(), 164, 289 coef(), 111, 157, 247, 251 confint(), 111 contour(), 46 contrasts(), 118, 157 cor(), 44, 122, 155, 416 cumsum(), 404 cut(), 292 cutree(), 406 cv.glm(), 192, 193, 199 cv.glmnet(), 254 cv.tree(), 326, 328, 334 data.frame(), 171, 201, 262, 324 dev.off(), 46 dim(), 48, 49 dist(), 406, 416 fix(), 48, 54 for(), 193 gam(), 284, 294, 296 gbm(), 330 glm(), 156, 161, 192, 199, 291 glmnet(), 251, 253–255 hatvalues(), 113 hclust(), 406, 407 hist(), 50, 55 I(), 115, 289, 291, 296 identify(), 50 ifelse(), 324 image(), 46 importance(), 330, 333, 334 is.na(), 244 jitter(), 292 jpeg(), 46 kmeans(), 404, 405 knn(), 163, 164 lda(), 161, 162 legend(), 125 length(), 43 library(), 109, 110 lines(), 112 lm(), 110, 112, 113, 115, 116, 121, 122, 156, 161, 191, 192, 254, 256, 288, 294, 324 lo(), 296 loadhistory(), 51 loess(), 294 ls(), 43 matrix(), 44 mean(), 45, 158, 191, 401 median(), 171 model.matrix(), 251 na.omit(), 49, 244 names(), 49, 111 ns(), 293 pairs(), 50, 55 par(), 112, 289 pcr(), 256, 258 pdf(), 46 persp(), 47 plot(), 45, 46, 49, 55, 112, 122, 246, 295, 325, 360, 371, 406, 408 plot.gam(), 295 plot.svm(), 360 plsr(), 258 points(), 246 poly(), 116, 191, 288–290, 299 prcomp(), 402, 403, 416 predict(), 111, 157, 160, 162, 163, 191, 249, 250, 252, 253, 289, 291, 292, 296, 325, 327, 361, 364, 365 print(), 172 prune.misclass(), 327 prune.tree(), 328 q(), 51 qda(), 162 quantile(), 201 rainbow(), 408 randomForest(), 329 range(), 56 read.csv(), 49, 54, 418
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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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Page 434 and 435: Index C p , 78, 205, 206, 210-213 R
Page 436 and 437: Index 421 Wage, 1, 2, 9, 10, 14, 26
Page 440 and 441: Index 425 read.table(), 48, 49 regs
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