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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

Why does boosting work?

The Adaptive boosting algorithm section in the previous chapter contained m models, classifiers Why does boosting work?, n observations and weights, and a voting power that is determined sequentially. The adaptation of the adaptive boosting method was illustrated using a toy example, and then applied using specialized functions. When compared with the bagging and random forest methods, we found that boosting provides the highest accuracy, which you may remember from the results in the aforementioned section in the previous chapter. However, the implementation of the algorithm does not tell us why it was expected to perform better.

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Publisher Resources

ISBN: 9781788624145Supplemental Content