Classification and Regression TreesThe methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties. |
Contents
INTRODUCTION TO TREE CLASSIFICATION | 18 |
RIGHT SIZED TREES AND HONEST ESTIMATES | 59 |
SPLITTING RULES | 93 |
Copyright | |
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Classification and Regression Trees Leo Breiman,Jerome Friedman,R.A. Olshen,Charles J. Stone Limited preview - 2017 |
Common terms and phrases
accuracy algorithm B₁ Bayes rule best split bromine C(ij C₁ CART categorical variables Chapter Class 1 Node class probability estimation computed contains corresponding cost-complexity cross-validation estimates data sets decrease defined DEFINITION denote digit recognition distribution equal example Figure function Gini index Gini splitting given heart attack large number learning sample LEMMA linear combination linear regression m₁ mass spectra maximizes mean squared error measurement vectors method minimizes misclassification rate missing values nearest neighbor node impurity nonterminal node optimally pruned subtree p(jt P₁ partition patients percent predicted predictor priors procedure proof pruning algorithm R(t₁ random variables regression trees result risk Section sequence splitting rule standard error subsampling subsets Suppose surrogate splits t₁ T₂ T8 cells TABLE terminal nodes test sample estimates Theorem tion tree construction tree grown tree selected tree structured tree structured classification two-class variable importance variance waveform x₁ α α