Distribution-Free Calibration Guarantees for Histogram Binning Without Sample Splitting
Abstract
We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan (2001). Histogram binning has displayed strong practical performance, but theoretical guarantees have only been shown for sample split versions that avoid ’double dipping’ the data. We demonstrate that the statistical cost of sample splitting is practically significant on a credit default dataset. We then prove calibration guarantees for the original method that double dips the data, using a certain Markov property of order statistics. Based on our results, we make practical recommendations for choosing the number of bins in histogram binning. In our illustrative simulations, we propose a new tool for assessing calibration—validity plots—which provide more information than an ECE estimate.
Cite
Text
Gupta and Ramdas. "Distribution-Free Calibration Guarantees for Histogram Binning Without Sample Splitting." International Conference on Machine Learning, 2021.Markdown
[Gupta and Ramdas. "Distribution-Free Calibration Guarantees for Histogram Binning Without Sample Splitting." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/gupta2021icml-distributionfree/)BibTeX
@inproceedings{gupta2021icml-distributionfree,
title = {{Distribution-Free Calibration Guarantees for Histogram Binning Without Sample Splitting}},
author = {Gupta, Chirag and Ramdas, Aaditya},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {3942-3952},
volume = {139},
url = {https://mlanthology.org/icml/2021/gupta2021icml-distributionfree/}
}