In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation.
May 29, 2023
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In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-decrease (or “parity") event ...
Aug 1, 2023 · Calibration in regression does not imply parity calibration. Page 16. Slide 16. Parity Calibration via Post-hoc Calibration. -. Online learn ...
i=1 | pi,obs − pi |. • Parity Calibration: reliability diagram and calibration error. For parity calibration, we produce the reliability diagram following ...
While one may expect a calibrated regression model to produce calibrated parity probabilities, this is not the case, for many.
Some notions of “fairness” for classification: Don't use protected features directly, Have equal measures of error across groups, Calibration.
The fact that error parity and calibration are mutually exclusive has been most significant in terms of public debate. Theorem 5.7. Assume the groups have ...
Delve into the concept of parity calibration, which addresses the challenge of predicting whether future observations will increase or decrease compared to ...
In summary, we conclude that maintaining cost parity and calibration is desirable yet often difficult in practice. Although we provide an algorithm to ...
A parity measure is a simple observational criterion that requires on the evaluation metrics to be independent of the salient group A.