Classification And Regression Trees Breiman Pdfl
Classification and Regression Trees Breiman pdf
Classification and regression trees are a type of machine learning method that can be used to analyze data and make predictions based on the structure of the data. They are also known as decision trees, because they can be represented as a tree-like diagram that shows the rules for splitting the data into different groups or outcomes.
Download Zip: https://ssurll.com/2w4niX
The book Classification and Regression Trees by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone is a classic reference on this topic. It was first published in 1984 and covers both the practical and theoretical aspects of tree methods. The book explains how to use trees as a data analysis method, and also proves some of their fundamental properties in a mathematical framework.
The book is divided into 12 chapters, each focusing on a different aspect of tree methods. Some of the topics covered are:
Introduction to tree classification: how to construct and interpret tree rules for categorical data.
Right sized trees and honest estimates: how to avoid overfitting and underfitting by choosing the optimal size of the tree.
Splitting rules: how to select the best variable and threshold for splitting the data at each node of the tree.
Strengthening and interpreting: how to improve the accuracy and interpretability of the tree by pruning, smoothing, or combining multiple trees.
Medical diagnosis and prognosis: how to apply tree methods to real-world problems in medicine, such as predicting the risk of heart attack or survival time.
Mass spectra classification: how to use tree methods to classify chemical compounds based on their mass spectra.
Regression trees: how to extend tree methods to continuous data and predict numerical values.
Bayes rules and partitions: how to relate tree methods to Bayesian statistics and optimal partitions of the data space.
Optimal pruning: how to find the best subtree within a larger tree using dynamic programming.
Construction of trees from a learning sample: how to estimate the error rate of a tree using cross-validation or bootstrap methods.
Consistency: how to prove that tree methods converge to the true underlying model as the sample size increases.
The book is available online as a pdf file from various sources. One of them is [Taylor & Francis e Book], which provides access to the full text of the book with a subscription or purchase. Another source is [Harvard University], which offers a pdf file of chapter 11 on classification algorithms and regression trees for free download. A third source is [Google Books], which allows previewing some pages of the book for free.
Classification and regression trees are a powerful and versatile tool for data analysis and prediction. The book by Breiman et al. is a comprehensive and authoritative guide on this topic, suitable for both practitioners and researchers who want to learn more about this method.