When using machine learning for fault detection, a common problem is the fact
that most data sets are very unbalanced, with the minority class (a fault)
being the interesting one. In this paper, we investigate the usage of
Venn-Abers predictors, looking specifically at the effect on the minority class
predictions. A key property of Venn-Abers predictors is that they output
well-calibrated probability intervals. In the experiments, we apply Venn-Abers
calibration to decision trees, random forests and XGBoost models, showing how
both overconfident and underconfident models are corrected. In addition, the
benefit of using the valid probability intervals produced by Venn-Abers for
decision support is demonstrated. When using techniques producing opaque
underlying models, e.g., random forest and XGBoost, each prediction will
consist of not only the label, but also a valid probability interval, where the
width is an indication of the confidence in the estimate. Adding Venn-Abers on
top of a decision tree allows inspection and analysis of the model, to
understand both the underlying relationship, and finding out in which parts of
feature space that the model is accurate and/or confident.

本文研究了使用 Venn-Abers 预测器进行故障检测的效果，发现其可以很好地解决机器学习中数据不平衡的问题，并且能够产生良好的校准概率区间，改善模型的可信度。同时，将 Venn-Abers 与决策树结合，可以更好地理解模型关系，进而得到更加精确的预测结果。

使用 Venn-Abers 进行良好校准的概率预测维护

Well-Calibrated Probabilistic Predictive Maintenance using Venn-Abers

This paper continues study, both theoretical and empirical, of the method of
Venn prediction, concentrating on binary prediction problems. Venn predictors
produce probability-type predictions for the labels of test objects which are
guaranteed to be well calibrated under the standard assumption that the
observations are generated independently from the same distribution. We give a
simple formalization and proof of this property. We also introduce Venn-Abers
predictors, a new class of Venn predictors based on the idea of isotonic
regression, and report promising empirical results both for Venn-Abers
predictors and for their more computationally efficient simplified version.

本文研究了 Venn 预测法在二元预测问题上的应用，证明了其输出结果的良好校准性，并介绍了一种基于保序回归原理的 Venn-Abers 预测器，通过简化算法提高了计算效率并取得了良好的实验结果。