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Fair evaluation of classifier predictive performance based on binary confusion matrix

Author

Listed:
  • Amalia Vanacore

    (University of Naples Federico II)

  • Maria Sole Pellegrino

    (University of Naples Federico II)

  • Armando Ciardiello

    (University of Naples Federico II)

Abstract

Evaluating the ability of a classifier to make predictions on unseen data and increasing it by tweaking the learning algorithm are two of the main reasons motivating the evaluation of classifier predictive performance. In this study the behavior of Balanced $$AC_1$$ A C 1 — a novel classifier accuracy measure — is investigated under different class imbalance conditions via a Monte Carlo simulation. The behavior of Balanced $$AC_1$$ A C 1 is compared against that of several well-known performance measures based on binary confusion matrix. Study results reveal the suitability of Balanced $$AC_1$$ A C 1 with both balanced and imbalanced data sets. A real example of the effects of class imbalance on the behavior of the investigated classifier performance measures is provided by comparing the performance of several machine learning algorithms in a churn prediction problem.

Suggested Citation

  • Amalia Vanacore & Maria Sole Pellegrino & Armando Ciardiello, 2024. "Fair evaluation of classifier predictive performance based on binary confusion matrix," Computational Statistics, Springer, vol. 39(1), pages 363-383, February.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01301-9
    DOI: 10.1007/s00180-022-01301-9
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