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Hands-On Ensemble Learning with R
book

Hands-On Ensemble Learning with R

by Prabhanjan Narayanachar Tattar
July 2018
Beginner to intermediate content levelBeginner to intermediate
376 pages
9h 1m
English
Packt Publishing
Content preview from Hands-On Ensemble Learning with R

The general boosting algorithm

The tree-based ensembles in the previous chapters, Bagging and Random Forests, cover an important extension of the decision trees. However, while bagging provides greater stability by averaging multiple decision trees, the bias persists. This limitation motivated Breiman to sample the covariates at each split point to generate an ensemble of "independent" trees and lay the foundation for random forests. The trees in the random forests can be developed in parallel, as is the case with bagging. The idea of averaging over multiple trees is to ensure the balance between the bias and variance trade-off. Boosting is the third most important extension of the decision trees, and probably the most effective one. It is again ...

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Publisher Resources

ISBN: 9781788624145Supplemental Content