Improving Confidence Estimates for Unfamiliar Examples
This addresses the issue of unreliable confidence estimates in machine learning models for practitioners, though it is incremental as it builds on existing techniques.
The paper tackles the problem of overconfident predictions on unfamiliar examples, showing that a gender classifier is 12 times more likely to be wrong with 99% confidence when faced with subjects from an unseen age group. It evaluates methods like calibration and ensembles, finding that an ensemble of calibrated models performs best overall.
Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with relevant but unfamiliar examples. A classifier we trained to recognize gender is 12 times more likely to be wrong with a 99% confident prediction if presented with a subject from a different age group than those seen during training. In this paper, we compare and evaluate several methods to improve confidence estimates for unfamiliar and familiar samples. We propose a testing methodology of splitting unfamiliar and familiar samples by attribute (age, breed, subcategory) or sampling (similar datasets collected by different people at different times). We evaluate methods including confidence calibration, ensembles, distillation, and a Bayesian model and use several metrics to analyze label, likelihood, and calibration error. While all methods reduce over-confident errors, the ensemble of calibrated models performs best overall, and T-scaling performs best among the approaches with fastest inference. Our code is available at https://github.com/lizhitwo/ConfidenceEstimates . $\color{red}{\text{Please see UPDATED ERRATA.}}$