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The class imbalance problem

Nature Methods volume 18, pages 1270–1272 (2021)Cite this article

The exception proves the rule.

We previously discussed how classifiers based on logistic regression1 and decision trees2 can be used for predicting the class of an observation. Unfortunately, when such classifiers are trained3 on a dataset in which one of the response classes is rare, they can underestimate the probability of observing a rare event — the greater the imbalance, the greater this small-sample bias4. This month, we illustrate how to mitigate the negative effect of class imbalance on the training of classifiers.

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Fig. 1: Rebalancing methods applied to a dataset with M = 50 normal and m = 5 diseased cells (IR = 10).
Fig. 2: Benefits of rebalancing classes in the training set by undersampling with increasing imbalance ratio on the performance of a CART decision tree classifier.
Fig. 3: Training with AUROC can provide acceptable performance at IR = 10 without rebalancing.
Fig. 4: Comparison of rebalancing methods on the performance of a CART decision tree classifier.

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Authors and Affiliations

  1. Farmer School of Business, Miami University, Oxford, OH, USA

    Fadel M. Megahed

  2. Department of Mathematics, University of Dayton, Dayton, OH, USA

    Ying-Ju Chen

  3. IBM Research – Almaden, San Jose, CA, USA

    Aly Megahed & Yuya Ong

  4. Department of Statistics, The Pennsylvania State University, State College, PA, USA

    Naomi Altman

  5. Canada’s Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada

    Martin Krzywinski

Authors
  1. Fadel M. Megahed
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  2. Ying-Ju Chen
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  3. Aly Megahed
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  4. Yuya Ong
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  5. Naomi Altman
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  6. Martin Krzywinski
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Corresponding author

Correspondence to Martin Krzywinski.

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The authors declare no competing interests.

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Megahed, F.M., Chen, YJ., Megahed, A. et al. The class imbalance problem. Nat Methods 18, 1270–1272 (2021). https://doi.org/10.1038/s41592-021-01302-4

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