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2.4 Exercises 57 (f) What is the median pupil-teacher ratio among the towns in this data set? (g) Which suburb of Boston has lowest median value of owneroccupied homes? What are the values of the other predictors for that suburb, and how do those values compare to the overall ranges for those predictors? Comment on your findings. (h) In this data set, how many of the suburbs average more than seven rooms per dwelling? More than eight rooms per dwelling? Comment on the suburbs that average more than eight rooms per dwelling.
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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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128 4. Classification 4.1 An Overvi
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134 4. Classification Coefficient S
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140 4. Classification 0 1 2 3 4 5
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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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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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204 6. Linear Model Selection and R
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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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8.4 Exercises 333 X2 < 1 15 X 2 1 0
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338 9. Support Vector Machines 9.1
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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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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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