Classification and Regression TreesRoutledge, 19 oct 2017 - 368 páginas 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. |
Índice
Preface | |
INTRODUCTION TO TREE CLASSIFICATION | |
RIGHT SIZED TREES AND HONESTESTIMATES | |
SPLITTING RULES | |
STRENGTHENING AND INTERPRETING | |
MEDICAL DIAGNOSIS AND PROGNOSIS | |
MASS SPECTRA CLASSIFICATION | |
BAYES RULES AND PARTITIONS | |
CONSTRUCTION OF TREES FROM A LEARNING SAMPLE | |
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accuracy algorithm Bayes rule best split bromine CART categorical variables Chapter class probability estimation classification problem classification rule computed consists contains corresponding cross-validation estimates cross-validation trees data sets defined DEFINITION denote digit recognition distribution equal estimate of R*(T example Figure Gini index given grown heart attack independent large number learning sample LEMMA limN linear regression mass spectra maximizes mean squared error measurement vectors method misclassification rate missing values nearest neighbor node impurity nonterminal node Olshen optimally pruned subtree partition patients percent pN(t prediction predictor prior probabilities priors procedure proof pruning algorithm random variables recognition data regression trees risk root node Section sequence splitting criterion splitting rule standard error subsampling subsets Suppose surrogate splits TABLE terminal nodes test sample estimates Theorem tree construction tree growing tree selected tree structured tree structured classification variable importance variance waveform