Estimating Classification Confidence Using Kernel Densities

TL;DR

This paper presents new kernel density ratio algorithms for post-hoc confidence calibration in exploratory classification tasks, emphasizing the importance of category-specific calibration and visual sanity checks.

Contribution

It introduces novel kernel density ratio methods and a bandwidth selection algorithm for improved confidence calibration in exploratory classification problems.

Findings

Kernel density ratio calibration improves confidence estimates.

Category-specific calibration outperforms full response matrix methods.

Calibration should be test-data-only and visually validated.

Abstract

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually.