将Venn-Abers预测扩展至无界回归，提升大样本下的预测效率。
Inductive Venn-Abers and related regressors
- 结合自适应校准与分组方法，构建无界回归的校准预测器
- 在大规模训练集上，点估计性能优于传统回归器
- 适合对预测可靠性要求高的场景，如金融或医疗建模
Venn-Abers预测器具有良好的有效性，但此前仅适用于二分类问题，最近已扩展至有界回归。本文将其推广到无界回归场景，引入了共形预测元素。通过模拟和实证研究，我们评估了由Venn-Abers回归器导出的点回归器的预测效率，结果表明：在较大训练集条件下，其预测性能较标准回归器有所提升。
原文摘要 · Abstract (English)
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.
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