用随机森林提升深度模型预测概率的准确性
ForeCal: Random Forest-based Calibration for DNNs
- 基于随机森林构建非参数校准方法，利用其单调性和范围保持特性
- 在43个数据集上，预期校准误差比现有方法降低15%以上
- 适合需要可靠置信度估计的医疗、金融等高风险场景
基于深度神经网络（DNN）的分类器在区分样本方面表现优异，但其输出概率常与真实事件发生率不一致。后处理校准算法常用于修正这一问题。现有方法如等距回归、Platt缩放和温度缩放虽有效，但受限于参数假设或无法捕捉复杂非线性关系。本文提出一种基于随机森林的新型后处理校准算法ForeCal，利用随机森林的弱单调性和范围保持特性，能更有效地实现校准。该方法为非参数化，可引入外部信息作为特征以学习更优校准函数。在来自UCI机器学习库的43个多样化数据集上的实验表明，ForeCal在预期校准误差（ECE）上优于现有最先进方法，且对基线DNN的判别能力（以AUC衡量）影响极小。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。