Confidence region prediction is a practically useful extension to the commonly studied pattern recognition problem. Instead of predicting a single label, the constraint is relaxed to allow prediction of a subset of labels given a desired confidence level 1-delta. Ideally, effective region predictions should be (1) well calibrated - predictive regions at confidence level 1-delta should err with relative frequency at most delta and (2) be as narrow (or certain) as possible. We present a simple technique to generate confidence region predictions from conditional probability estimates (probability forecasts). We use this 'conversion' technique to generate confidence region predictions from probability forecasts output by standard machine learning algorithms when tested on 15 multi-class datasets. Our results show that approximately 44% of experiments demonstrate well-calibrated confidence region predictions, with the K-Nearest Neighbour algorithm tending to perform consistently well across all data. Our results illustrate the practical benefits of effective confidence region prediction with respect to medical diagnostics, where guarantees of capturing the true disease label can be given.

从条件概率估计（概率预测）生成信心区预测的简单技术用于标准机器学习算法在15个多类数据集上的测试，结果显示约44％的实验展示了良好校准的信心区预测，其中最近邻算法在所有数据上表现出色，这证明了有效信心区预测在医学诊断方面的实际益处。

使用概率预测器确定有效的置信区间预测