Classification and Regression TreesRoutledge, 19 ott 2017 - 368 pagine 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. |
Sommario
| 1 | |
INTRODUCTION TO TREE CLASSIFICATION | 18 |
RIGHT SIZED TREES AND HONEST ESTIMATES | 59 |
SPLITTING RULES | 93 |
STRENGTHENING AND INTERPRETING | 130 |
MEDICAL DIAGNOSIS AND PROGNOSIS | 174 |
MASS SPECTRA CLASSIFICATION | 203 |
REGRESSION TREES | 216 |
BAYES RULES AND PARTITIONS | 266 |
OPTIMAL PRUNING | 279 |
CONSTRUCTION OF TREES FROM A LEARNING SAMPLE | 297 |
CONSISTENCY | 318 |
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accuracy accurate algorithm applications assumed Bayes best split called CART Chapter class probability classification combination complete computed Consider consists constructed contains continuous corresponding criterion cross-validation data sets decrease defined DEFINITION denote digit discussed distribution effect equal error estimate example Figure final follows function Gini given gives growing grown heart holds important impurity independent learning sample least less linear loss mean measurement method minimizes misclassification cost misclassification rate missing Observe optimal ordered original partition patients percent possible predicted predictor priors probability problem procedure produce proof proportion pruning questions random range recognition reduction regression resubstitution estimate result risk rule selected sequence split squares standard subsets Suppose Table terminal nodes test sample Theorem tion tree structured true variables variance vector waveform