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Springer Texts in Statistics 103 Series Editors: G. Casella S. Fienberg I. Olkin For further volumes: http://www.springer.com/series/417
Page 1: Springer Texts in Statistics Gareth
Page 5 and 6: Gareth James Department of Informat
Page 8 and 9: Preface Statistical learning refers
Page 10 and 11: Contents Preface vii 1 Introduction
Page 12 and 13: Contents xi 4.6.4 Quadratic Discrim
Page 14 and 15: Contents xiii 8.3.3 Bagging and Ran
Page 16 and 17: 1 Introduction An Overview of Stati
Page 18 and 19: 1. Introduction 3 Yesterday Two Day
Page 20 and 21: 1. Introduction 5 Z2 −60 −40
Page 22 and 23: 1. Introduction 7 learning was star
Page 24 and 25: 1. Introduction 9 R years before th
Page 26 and 27: 1. Introduction 11 The T notation d
Page 28 and 29: 1. Introduction 13 are discussed in
Page 30 and 31: 2 Statistical Learning 2.1 What Is
Page 32 and 33: 2.1 What Is Statistical Learning? 1
Page 36 and 37: 2.1.2 How Do We Estimate f? 2.1 Wha
Page 44 and 45: 2.2 Assessing Model Accuracy 29 met
Page 46 and 47: 2.2 Assessing Model Accuracy 31 Y 2
Page 48 and 49: 2.2 Assessing Model Accuracy 33 Y 2
Page 50 and 51: 2.2 Assessing Model Accuracy 35 gre
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2.2.3 The Classification Setting 2.
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2.2 Assessing Model Accuracy 39 whe
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2.2 Assessing Model Accuracy 41 KNN
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2.3 Lab: Introduction to R 43 and i
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2.3 Lab: Introduction to R 45 > x=r
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2.3 Lab: Introduction to R 47 known
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2.3 Lab: Introduction to R 49 Note
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2.3 Lab: Introduction to R 51 > plo
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2.4 Exercises 53 the amount of flex
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2.4 Exercises 55 (c) > rownames(col
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2.4 Exercises 57 (f) What is the me
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60 3. Linear Regression evidence of
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62 3. Linear Regression Sales 5 10
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64 3. Linear Regression Y −10 −
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66 3. Linear Regression where σ is
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68 3. Linear Regression then we can
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70 3. Linear Regression To calculat
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72 3. Linear Regression Simple regr
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74 3. Linear Regression Coefficient
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76 3. Linear Regression Quantity Va
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78 3. Linear Regression F-statistic
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80 3. Linear Regression significant
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82 3. Linear Regression 2. Of cours
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90 3. Linear Regression Balance 200
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94 3. Linear Regression are based o
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96 3. Linear Regression Response Y
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98 3. Linear Regression Y 0 5 10 20
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100 3. Linear Regression βAge −5
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102 3. Linear Regression collineari
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104 3. Linear Regression 6. Is the
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106 3. Linear Regression In what se
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108 3. Linear Regression y y 0.5 1.
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110 3. Linear Regression ISLR, must
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112 3. Linear Regression > predict(
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114 3. Linear Regression Residuals
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116 3. Linear Regression Coefficien
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118 3. Linear Regression Given a qu
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120 3. Linear Regression 3.7 Exerci
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122 3. Linear Regression i. Is ther
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124 3. Linear Regression (These for
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126 3. Linear Regression (d) Now fi
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128 4. Classification 4.1 An Overvi
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130 4. Classification which would i
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132 4. Classification 0 or 1, in pr
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134 4. Classification Coefficient S
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136 4. Classification Coefficient S
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138 4. Classification analysis, is
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140 4. Classification 0 1 2 3 4 5
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142 4. Classification x 2 x 2 x 1 x
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144 4. Classification are the Bayes
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146 4. Classification True default
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148 4. Classification ROC Curve Tru
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150 4. Classification X 2 −4 −3
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152 4. Classification SCENARIO 1 SC
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154 4. Classification Scenario 6: D
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156 4. Classification Lag1 0.0409 -
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158 4. Classification > contrasts (
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160 4. Classification [1] 0.48 > me
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162 4. Classification The predict()
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164 4. Classification 3. A vector c
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166 4. Classification We fit a KNN
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168 4. Classification 4.7 Exercises
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170 4. Classification (d) True or F
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172 4. Classification (b) Explore t
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5 Resampling Methods Resampling met
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5.1 Cross-Validation 177 1 2 3 n 7
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5.1 Cross-Validation 183 When we pe
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5.1 Cross-Validation 185 Degree=1 o
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5.2 The Bootstrap 187 5.2 The Boots
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5.2 The Bootstrap 189 0 50 100 150
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5.3.1 The Validation Set Approach 5
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5.3 Lab: Cross-Validation and the B
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5.3 Lab: Cross-Validation and the B
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5.4 Exercises 197 > boot.fn=functio
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5.4 Exercises 199 ii. Fit a multipl
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5.4 Exercises 201 i. Y = β 0 + β
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204 6. Linear Model Selection and R
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266 7. Moving Beyond Linearity •
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268 7. Moving Beyond Linearity at e
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270 7. Moving Beyond Linearity Pr(y
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272 7. Moving Beyond Linearity Piec
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274 7. Moving Beyond Linearity Wage
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276 7. Moving Beyond Linearity Mean
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278 7. Moving Beyond Linearity that
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280 7. Moving Beyond Linearity Smoo
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282 7. Moving Beyond Linearity Algo
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284 7. Moving Beyond Linearity
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286 7. Moving Beyond Linearity ▲
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288 7. Moving Beyond Linearity HS C
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290 7. Moving Beyond Linearity betw
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292 7. Moving Beyond Linearity > pf
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294 7. Moving Beyond Linearity > li
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296 7. Moving Beyond Linearity AIC:
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298 7. Moving Beyond Linearity (e)
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300 7. Moving Beyond Linearity (b)
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8 Tree-Based Methods In this chapte
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8.1 The Basics of Decision Trees 30
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Algorithm 8.1 Building a Regression
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8.2 Bagging, Random Forests, Boosti
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8.3 Lab: Decision Trees 325 We see
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8.3 Lab: Decision Trees 327 We now
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8.3 Lab: Decision Trees 329 install
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8.3 Lab: Decision Trees 331 > summa
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8.4 Exercises 333 X2 < 1 15 X 2 1 0
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8.4 Exercises 335 (b) Create a trai
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338 9. Support Vector Machines 9.1
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340 9. Support Vector Machines X2
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348 9. Support Vector Machines −3
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350 9. Support Vector Machines such
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352 9. Support Vector Machines vect
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354 9. Support Vector Machines True
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356 9. Support Vector Machines SVM
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358 9. Support Vector Machines Loss
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360 9. Support Vector Machines The
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362 9. Support Vector Machines > yp
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364 9. Support Vector Machines SVM-
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366 9. Support Vector Machines > fi
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368 9. Support Vector Machines We s
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370 9. Support Vector Machines (a)
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372 9. Support Vector Machines (a)
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374 10. Unsupervised Learning grasp
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376 10. Unsupervised Learning compo
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378 10. Unsupervised Learning −0.
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380 10. Unsupervised Learning Secon
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382 10. Unsupervised Learning In ce
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384 10. Unsupervised Learning we wo
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386 10. Unsupervised Learning numbe
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388 10. Unsupervised Learning Now,
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390 10. Unsupervised Learning 320.9
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392 10. Unsupervised Learning 0 2 4
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394 10. Unsupervised Learning The t
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396 10. Unsupervised Learning 9 9 X
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398 10. Unsupervised Learning 0 5 1
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400 10. Unsupervised Learning • I
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402 10. Unsupervised Learning Not s
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404 10. Unsupervised Learning The r
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406 10. Unsupervised Learning 10.5.
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408 10. Unsupervised Learning 10.6.
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410 10. Unsupervised Learning PVE 0
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412 10. Unsupervised Learning clust
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414 10. Unsupervised Learning (b) R
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416 10. Unsupervised Learning Appli
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418 10. Unsupervised Learning (a) L
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420 Index Boolean, 159 boosting, 12
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422 Index inner product, 351 input
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424 Index bs(), 293, 300 c(), 43 cb
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426 Index soft-thresholding, 225 sp
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