论文标题
通过建模机器学习来校准班级重量
Calibrating for Class Weights by Modeling Machine Learning
论文作者
论文摘要
一个众所周知的问题是，将机器学习算法提供的置信度得分在多大程度上被校准为地面真实概率。我们的起点是，校准似乎与班级加权不兼容，这是一种通常不常见（阶级失衡）或希望实现一些外部目标（成本敏感学习）的技术。我们为这种不兼容提供了基于模型的解释，并使用我们的拟人化模型来生成一种从算法中恢复似然的简单方法，该算法因班级权重而被错误校准。我们在Rajpurkar，Irvin，Zhu等的二元肺炎检测任务中验证了这种方法。 （2017）。
A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a technique often employed when one class is less common (class imbalance) or with the hope of achieving some external objective (cost-sensitive learning). We provide a model-based explanation for this incompatibility and use our anthropomorphic model to generate a simple method of recovering likelihoods from an algorithm that is miscalibrated due to class weighting. We validate this approach in the binary pneumonia detection task of Rajpurkar, Irvin, Zhu, et al. (2017).
