Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they are applicable only to the case of binary classification, with a recent extension to bounded regression. We generalize them to the case of unbounded regression, which requires adding an element of conformal prediction. In our simulation and empirical studies we investigate the predictive efficiency of point regressors derived from Venn-Abers regressors and argue that they somewhat improve the predictive efficiency of standard regressors for larger training sets.

翻译：Venn-Abers预测器是一种具有良好有效性属性的概率预测器，但其主要局限在于仅适用于二元分类，且近期才有扩展至有界回归的应用。我们将其推广至无界回归情况，这需要引入共形预测的元素。通过仿真和实证研究，我们探究了源自Venn-Abers回归器的点回归器的预测效率，并论证其在较大训练集上能略微提升标准回归器的预测效率。