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Does Ensemble Learning Always Lead to Better Forecasts?

Hitoshi Hamori and Shigeyuki Hamori ()

Applied Economics and Finance, 2020, vol. 7, issue 2, 51-56

Abstract: Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.

JEL-codes: R00 Z0 (search for similar items in EconPapers)
Date: 2020
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