Precision/Recall on Imbalanced Test Data

Hongwei Shang, Jean-Marc Langlois, Kostas Tsioutsiouliklis, Changsung Kang
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:9879-9891, 2023.

Abstract

In this paper we study the problem of estimating accurately the precision and recall for binary classification when the classes are imbalanced and only a limited number of human labels are available. One common strategy is to over-sample the small positive class predicted by the classifier. Rather than random sampling where the values in a confusion matrix are observations coming from a multinomial distribution, we over-sample the minority positive class predicted by the classifier, resulting in two independent binomial distributions. But how much should we over-sample? And what confidence/credible intervals can we deduce based on our over-sampling? We provide formulas for (1) the confidence intervals of the adjusted precision/recall after over-sampling; (2) Bayesian credible intervals of adjusted precision/recall. For precision, the higher the over-sampling rate, the narrower the confidence/credible interval. For recall, there exists an optimal over-sampling ratio, which minimizes the width of the confidence/credible interval. Also, we present experiments on synthetic data and real data to demonstrate the capability of our method to construct accurate intervals. Finally, we demonstrate how we can apply our techniques to Yahoo mail’s quality monitoring system.

Cite this Paper


BibTeX
@InProceedings{pmlr-v206-shang23a, title = {Precision/Recall on Imbalanced Test Data}, author = {Shang, Hongwei and Langlois, Jean-Marc and Tsioutsiouliklis, Kostas and Kang, Changsung}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {9879--9891}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/shang23a/shang23a.pdf}, url = {https://proceedings.mlr.press/v206/shang23a.html}, abstract = {In this paper we study the problem of estimating accurately the precision and recall for binary classification when the classes are imbalanced and only a limited number of human labels are available. One common strategy is to over-sample the small positive class predicted by the classifier. Rather than random sampling where the values in a confusion matrix are observations coming from a multinomial distribution, we over-sample the minority positive class predicted by the classifier, resulting in two independent binomial distributions. But how much should we over-sample? And what confidence/credible intervals can we deduce based on our over-sampling? We provide formulas for (1) the confidence intervals of the adjusted precision/recall after over-sampling; (2) Bayesian credible intervals of adjusted precision/recall. For precision, the higher the over-sampling rate, the narrower the confidence/credible interval. For recall, there exists an optimal over-sampling ratio, which minimizes the width of the confidence/credible interval. Also, we present experiments on synthetic data and real data to demonstrate the capability of our method to construct accurate intervals. Finally, we demonstrate how we can apply our techniques to Yahoo mail’s quality monitoring system.} }
Endnote
%0 Conference Paper %T Precision/Recall on Imbalanced Test Data %A Hongwei Shang %A Jean-Marc Langlois %A Kostas Tsioutsiouliklis %A Changsung Kang %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-shang23a %I PMLR %P 9879--9891 %U https://proceedings.mlr.press/v206/shang23a.html %V 206 %X In this paper we study the problem of estimating accurately the precision and recall for binary classification when the classes are imbalanced and only a limited number of human labels are available. One common strategy is to over-sample the small positive class predicted by the classifier. Rather than random sampling where the values in a confusion matrix are observations coming from a multinomial distribution, we over-sample the minority positive class predicted by the classifier, resulting in two independent binomial distributions. But how much should we over-sample? And what confidence/credible intervals can we deduce based on our over-sampling? We provide formulas for (1) the confidence intervals of the adjusted precision/recall after over-sampling; (2) Bayesian credible intervals of adjusted precision/recall. For precision, the higher the over-sampling rate, the narrower the confidence/credible interval. For recall, there exists an optimal over-sampling ratio, which minimizes the width of the confidence/credible interval. Also, we present experiments on synthetic data and real data to demonstrate the capability of our method to construct accurate intervals. Finally, we demonstrate how we can apply our techniques to Yahoo mail’s quality monitoring system.
APA
Shang, H., Langlois, J., Tsioutsiouliklis, K. & Kang, C.. (2023). Precision/Recall on Imbalanced Test Data. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:9879-9891 Available from https://proceedings.mlr.press/v206/shang23a.html.

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