Inductive Venn-Abers Predictive Distributions: New Applications & Evaluation

Venn-Abers predictors offer a distribution-free probabilistic framework that generates calibrated predictions from the outputs of scoring classifiers, relying on minimal assumptions about the data distribution. This paper explores the extension of this framework from classification to regression, producing predictive distributions. We show how to evaluate the efficacy of the framework by comparing various metrics that assess the accuracy and informativeness of the predictions. We also show that the framework can be used for real-time prediction, using datasets from predictive maintenance and energy consumption forecasting.