Calibration — Theory

Goal

The goal of binary calibration is to transform a score (typically given by an ML model) that is not a probability into a probability. The algorithms used for calibration can be interpreted as estimators of the confidence level.

Expected result: predicted scores closer to true probabilities after calibration.

Binary Calibration

We denote the \((h(X), y)\) pair as the score and ground truth. The model is calibrated if for every output \(q \in [0, 1]\):

\[ \Pr(Y = 1 \mid h(X) = q) = q \]

where \(h\) is the score predictor.

To apply calibration to a multi-class context, Gupta et al. propose a multiclass-to-binary (M2B) framework to reduce multi-class calibration to multiple binary calibrations.

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Top-Label Calibration

Top-Label calibration ¹ calibrates the model according to the highest score and the corresponding class. It applies binary calibration techniques (such as Platt scaling or isotonic regression) to multi-class calibration.

Let \(c\) be the classifier and \(h\) be the maximum score from the classifier. The couple \((c, h)\) is calibrated according to Top-Label calibration if:

\[ \Pr(Y = c(X) \mid h(X), c(X)) = h(X) \]

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References

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1. Gupta, C., and Ramdas, A. K. "Top-label calibration and multiclass-to-binary reductions." arXiv preprint arXiv:2107.08353 (2021). ↩