Voting and Bagging

Christo El Morr, Manar Jammal, Hossam Ali-Hassan and Walid El-Hallak ()
Additional contact information
Christo El Morr: York University
Manar Jammal: York University
Hossam Ali-Hassan: York University, Glendon Campus
Walid El-Hallak: Ontario Health

Chapter Chapter 14 in Machine Learning for Practical Decision Making, 2022, pp 413-430 from Springer

Abstract: Abstract The ensemble technique relies on the idea that aggregation of many classifiers and regressors will lead to a better prediction [1]. In this chapter, we will introduce the ensemble technique and cover two ways in which to organize an ensemble (literally, a set) of machine learning methods called voting and bagging [2] and one algorithm to perform bagging called random forest [1, 3]. The other two ways to organize the ensemble methods are called boosting and stacking, which will be covered in the next chapter.

Date: 2022
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DOI: 10.1007/978-3-031-16990-8_14

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