A01=Charles J. Stone
A01=Jerome Friedman
A01=Leo Breiman
A01=R.A. Olshen
advanced tree-based statistical methods
Author_Charles J. Stone
Author_Jerome Friedman
Author_Leo Breiman
Author_R.A. Olshen
Bayes Rule
Boston Housing Data
Category=PBT
Charles J. Stone
Class Probability Estimation
Cross-validation Estimate
Cross-validation Trees
Data Set
decision tree algorithms
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Expected Misclassification Cost
Jerome H. Friedman
Lad Regression
Learning Sample
Leo Breiman
Mass Spectra
medical data analysis
Minimum Systolic Blood Pressure
Misclassification Cost
Misclassification Rate
Multiple Linear Regression
multivariate data partitioning
Node Impurity
Noise Variables
Nonterminal Node
Optimal Pruning
predictive modelling techniques
Resubstitution Estimates
Richard A. Olshen
SE Rule
Split Criterion
Splitting Rule
statistical learning theory
supervised machine learning
Surrogate Split
Terminal Nodes
Test Sample Estimates
Product details
- ISBN 9781138469525
- Weight: 840g
- Dimensions: 156 x 234mm
- Publication Date: 29 Aug 2017
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
