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arXiv · 2305.00070

Online Platt Scaling with Calibeating

Chirag Gupta·Aaditya Ramdas
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Abstract

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OPS by incorporating a recently developed technique called calibeating to make it more robust. Theoretically, our resulting OPS+calibeating method is guaranteed to be calibrated for adversarial outcome sequences. Empirically, it is effective on a range of synthetic and real-world datasets, with and without distribution drifts, achieving superior performance without hyperparameter tuning. Finally, we extend all OPS ideas to the beta scaling method.

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Chirag Gupta, Aaditya Ramdas. 2023-04-28. Online Platt Scaling with Calibeating. https://arxiv.org/abs/2305.00070

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