Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration conditionally on uncertainty, i.e. consistency. I explore here several adaptations of BVS, in particular with alternative loss functions and a binning scheme based on an input-feature (X) in order to improve adaptivity, i.e. calibration conditional on X. The performances of BVS and its proposed variants are tested on a benchmark dataset for the prediction of atomization energies and compared to the results of isotonic regression.

翻译：近期提出的分箱方差缩放（BVS）作为一种机器学习回归问题中预测不确定性的后验重新校准方法，能够比均匀方差（或温度）缩放实现更高效的校正。原始BVS版本采用基于不确定度的分箱策略，旨在改善条件于不确定度的校准效果（即一致性）。本文探索了BVS的多种改进方案，特别是采用替代损失函数以及基于输入特征（X）的分箱策略，以提升自适应性（即条件于X的校准效果）。通过原子化能预测基准数据集对BVS及其改进变体的性能进行了测试，并与等渗回归结果进行了比较。