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Register a new userEstimating Expected Calibration Errors
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Added by
Nicolas Posocco
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive automatic decisions have to be taken. Studies have shown that most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities. Hence being able to calibrate these models, or enforce calibration while learning them, has regained interest in recent literature. In this context, properly assessing calibration is paramount to quantify new contributions tackling calibration. However, there is room for improvement for commonly used metrics and evaluation of calibration could benefit from deeper analyses. Thus this paper focuses on the empirical evaluation of calibration metrics in the context of classification. More specifically it evaluates different estimators of the Expected Calibration Error ($ECE$), amongst which legacy estimators and some novel ones, proposed in this paper. We build an empirical procedure to quantify the quality of these $ECE$ estimators, and use it to decide which estimator should be used in practice for different settings.
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Calibration of neural networks is a topical problem that is becoming increasingly important for real-world use of neural networks. The problem is especially noticeable when using modern neural networks, for which there is significant difference between the model confidence and the confidence it should have. Various strategies have been successfully proposed, yet there is more space for improvements. We propose a novel approach that introduces a differentiable metric for expected calibration error and successfully uses it as an objective for meta-learning, achieving competitive results with state-of-the-art approaches. Our approach presents a new direction of using meta-learning to directly optimize model calibration, which we believe will inspire further work in this promising and new direction.
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