ForeCal：基于随机森林的DNN校准
ForeCal: Random Forest-based Calibration for DNNs
AI总结：
ForeCal是一种基于随机森林的DNN事后校准方法，通过弱单调性和范围保持性实现更精确的校准，优于现有方法并在保持判别能力的同时降低校准误差。
摘要：
基于深度神经网络（DNN）的分类器在区分观测结果方面表现极为出色，导致更高的ROC AUC和准确性指标，但它们的输出往往在真实事件可能性上存在校准错误。事后校准算法常用于校准这些分类器的输出。像等距回归、Platt缩放和温度缩放等方法在某些情况下已被证明有效，但受到其参数假设和/或无法捕捉复杂非线性关系的限制。我们提出ForeCal——一种基于随机森林的新型事后校准算法。ForeCal利用随机森林的两个独特特性：能够强制弱单调性和范围保持性。它在实现校准方面比现有最先进方法更强大，是非参数的，并且可以将外生信息作为特征纳入学习更好的校准函数。通过在43个多样化的UCI ML数据集上的实验，我们证明ForeCal在期望校准误差（ECE）方面优于现有方法，且对基DNN的判别能力（以AUC衡量）影响最小。
英文摘要：
Deep neural network(DNN) based classifiers do extremely well in discriminating between observations, resulting in higher ROC AUC and accuracy metrics, but their outputs are often miscalibrated with respect to true event likelihoods. Post-hoc calibration algorithms are often used to calibrate the outputs of these classifiers. Methods like Isotonic regression, Platt scaling, and Temperature scaling have been shown to be effective in some cases but are limited by their parametric assumptions and/or their inability to capture complex non-linear relationships. We propose ForeCal - a novel post-hoc calibration algorithm based on Random forests. ForeCal exploits two unique properties of Random forests: the ability to enforce weak monotonicity and range-preservation. It is more powerful in achieving calibration than current state-of-the-art methods, is non-parametric, and can incorporate exogenous information as features to learn a better calibration function. Through experiments on 43 diverse datasets from the UCI ML repository, we show that ForeCal outperforms existing methods in terms of Expected Calibration Error(ECE) with minimal impact on the discriminative power of the base DNN as measured by AUC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
