Calibrating Where It Matters: Constrained Temperature Scaling

Stephen McKenna; Jacob Carse

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arXiv · 2406.11456

Calibrating Where It Matters: Constrained Temperature Scaling

Stephen McKenna·Jacob Carse

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Abstract

We consider calibration of convolutional classifiers for diagnostic decision making. Clinical decision makers can use calibrated classifiers to minimise expected costs given their own cost function. Such functions are usually unknown at training time. If minimising expected costs is the primary aim, algorithms should focus on tuning calibration in regions of probability simplex likely to effect decisions. We give an example, modifying temperature scaling calibration, and demonstrate improved calibration where it matters using convnets trained to classify dermoscopy images.

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Stephen McKenna, Jacob Carse. 2024-06-17. Calibrating Where It Matters: Constrained Temperature Scaling. https://arxiv.org/abs/2406.11456

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