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Does Ensemble Learning Always Lead to Better Forecasts?

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  • Hitoshi Hamori
  • Shigeyuki Hamori

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.

Suggested Citation

  • Hitoshi Hamori & Shigeyuki Hamori, 2020. "Does Ensemble Learning Always Lead to Better Forecasts?," Applied Economics and Finance, Redfame publishing, vol. 7(2), pages 51-56, March.
    • Handle: RePEc:rfa:aefjnl:v:7:y:2020:i:2:p:51-56
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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