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. Topics covered include an introduction to tree classification, right sized trees and honest estimates, splitting rules, and mass spectra classification.

Autorentext

Breiman, Leo

Inhalt
Preface, Chapter 1 BACKGROUND, Chapter 2 INTRODUCTION TO TREE CLASSIFICATION, Chapter 3 RIGHT SIZED TREES AND HONEST ESTIMATES, Chapter 4 SPLITTING RULES, Chapter 5 STRENGTHENING AND INTERPRETING, Chapter 6 MEDICAL DIAGNOSIS AND PROGNOSIS, Chapter 7 MASS SPECTRA CLASSIFICATION, Chapter 8 REGRESSION TREE, Chapter 9 BAYES RULES AND PARTITIONS, Chapter 10 OPTIMAL PRUNING, Chapter 11 CONSTRUCTION OF TREES FROM A LEARNING SAMPLE, Chapter 12 CONSISTENCY, Bibliography, Notation Index, Subject Index