Outsmarting machine learning – Ensemble methods

Ensemble learning has shown outstanding performance in a variety of machine learning applications by collaborating on forecasting from numerous core models. This article provides a compendious explanation for learning ensemble methods, including 3 primary methods: stacking, boosting and bagging. It...

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Bibliographic Details
Main Authors: Shah, Pragnesh, Patil, Harshali, Dongardive, Jyotshna
Format: Conference Proceeding
Language:English
Subjects:
Ensemble learning
Evolutionary algorithms
Machine learning
Online Access:Get full text
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Summary:Ensemble learning has shown outstanding performance in a variety of machine learning applications by collaborating on forecasting from numerous core models. This article provides a compendious explanation for learning ensemble methods, including 3 primary methods: stacking, boosting and bagging. It chronicles its evolution from the inceptive stages to modern cutting-edge algorithms. The purpose of study explores on popular ensemble techniques, namely AdaBoost (adaptive boosting), XGBoost (extreme gradient boosting), gradient boosting, random forest. A concerted effort is made to provide a brief exposition of their mathematical and algorithmic representations, filling a vacuum in the current literature that would be useful for both machine learning researchers and practitioners.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0227605