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Index 421 Wage, 1, 2, 9, 10, 14, 267, 269, 271, 272, 274–277, 280, 281, 283, 284, 286, 287, 299 Weekly, 14, 171, 200 decision tree, 12, 303–316 Default data set, 14, 128–137, 144–148, 198, 199 degrees of freedom, 32, 241, 271, 272, 278 dendrogram, 386, 390–396 density function, 138 dependent variable, 15 derivative, 272, 278 deviance, 206 dimension reduction, 204, 228–238 discriminant function, 141 dissimilarity, 396–398 distance correlation-based, 396–398, 416 Euclidean, 379, 387, 388, 394, 396–398 double-exponential distribution, 227 dummy variable, 82–86, 130, 134, 269 effective degrees of freedom, 278 elbow, 409 error irreducible, 18, 32 rate, 37 reducible, 18 term, 16 Euclidean distance, 379, 387, 388, 394, 396–398, 416 expected value, 19 exploratory data analysis, 374 F-statistic, 75 factor, 84 false discovery proportion, 147 false negative, 147 false positive, 147 false positive rate, 147, 149, 354 feature, 15 feature selection, 204 Fisher’s linear discriminant, 141 fit, 21 fitted value, 93 flexible, 22 for loop, 193 forward stepwise selection, 78, 207–208, 247 function, 42 Gaussian (normal) distribution, 138, 139, 142–143 generalized additive model, 6, 26, 265, 266, 282–287, 294 generalized linear model, 6, 156, 192 Gini index, 311–312, 319, 332 Heart data set, 312, 313, 317–320, 354, 355 heatmap, 47 heteroscedasticity, 95–96 hierarchical clustering, 390–396 dendrogram, 390–394 inversion, 395 linkage, 394–396 hierarchical principle, 89 high-dimensional, 78, 208, 239 hinge loss, 357 histogram, 50 Hitters data set, 14, 244, 251, 255, 256, 304, 305, 310, 311, 334 hold-out set, 176 hyperplane, 338–343 hypothesis test, 67–68, 75, 95 Income data set, 16–18, 22–24 independent variable, 15 indicator function, 268 inference, 17, 19
422 Index inner product, 351 input variable, 15 integral, 278 interaction, 60, 81, 87–90, 104, 286 intercept, 61, 63 interpretability, 203 inversion, 395 irreducible error, 18, 39, 82, 103 K-means clustering, 12, 386–389 K-nearest neighbors classifier, 12, 38–40, 127 regression, 104–109 kernel, 350–353, 356, 367 linear, 352 non-linear, 349–353 polynomial, 352, 354 radial, 352–354, 363 kernel trick, 351 Khan data set, 14, 366 knot, 266, 271, 273–275 Laplace distribution, 227 lasso, 12, 25, 219–227, 241–242, 309, 357 leaf, 305, 391 least squares, 6, 21, 61–63, 133, 203 line, 63 weighted, 96 level, 84 leverage, 97–99 likelihood function, 133 linear, 2, 86 linear combination, 121, 204, 229, 375 linear discriminant analysis, 6, 12, 127, 130, 138–147, 348, 354 linear kernel, 352 linear model, 20, 21, 59 linear regression, 6, 12 multiple, 71–82 simple, 61–71 linkage, 394–396, 410 average, 394–396 centroid, 394–396 complete, 391, 394–396 single, 394–396 local regression, 266, 294 logistic function, 132 logistic regression, 6, 12, 26, 127, 131–137, 286–287, 349, 356–357 multiple, 135–137 logit, 132, 286, 291 loss function, 277, 357 low-dimensional, 238 main effects, 88, 89 majority vote, 317 Mallow’s C p , 78, 205, 206, 210–213 margin, 341, 357 matrix multiplication, 12 maximal margin classifier, 337–343 hyperplane, 341 maximum likelihood, 132–133, 135 mean squared error, 29 misclassification error, 37 missing data, 49 mixed selection, 79 model assessment, 175 model selection, 175 multicollinearity, 243 multivariate Gaussian, 142–143 multivariate normal, 142–143 natural spline, 274, 278, 293 NCI60 data set, 4, 5, 14, 407, 409–412 negative predictive value, 147, 149 node internal, 305 purity, 311–312 terminal, 305
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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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Page 438 and 439: Index 423 noise, 22, 228 non-linear
Page 440 and 441: Index 425 read.table(), 48, 49 regs
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