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Distribution calibration for regression

Hao Song · Tom Diethe · Meelis Kull · Peter Flach

Pacific Ballroom #211

Keywords: [ Kernel Methods ] [ Gaussian Processes ] [ Bayesian Nonparametrics ] [ Approximate Inference ]


We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding the predicted target value. We introduce the novel concept of distribution calibration, and demonstrate its advantages over the existing definition of quantile calibration. We further propose a post-hoc approach to improving the predictions from previously trained regression models, using multi-output Gaussian Processes with a novel Beta link function. The proposed method is experimentally verified on a set of common regression models and shows improvements for both distribution-level and quantile-level calibration.

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