Classification and Regression Trees
The 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.
INTRODUCTION TO TREE CLASSIFICATION
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accuracy algorithm applications assumed Bayes best split called CART Chapter class probability classification combination complete computed Consider consists constructed contains corresponding cost criterion cross-validation data sets decrease defined DEFINITION denote digit discussed distribution effect equal error estimate example Figure follows function Gini given gives growing grown heart holds important impurity independent learning sample linear loss mean measure method minimizes misclassification cost misclassification rate missing Observe optimal ordered original partition patients percent possible predictive priors probability problem procedure produce proof proportion pruning questions random range recognition reduced regression resubstitution estimate result risk root rule selected sequence split squares standard subsets Suppose T₁ TABLE terminal nodes test sample Theorem tion tree structured true variables vector waveform