论文标题
重新思考日志赔率：线性概率建模和可解释机器学习的专家建议
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
论文作者
论文摘要
我们介绍了一个可解释的机器学习模型的家族，并提供了两个广泛的补充：线性化添加剂模型（LAM），这些模型（LAM）替换了一般添加剂模型（GAMS）中无处不在的逻辑链路函数；以及SubscaleHedge，一种专家建议算法，用于将基本模型组合在称为子量表的子集子集的基本模型中。 LAM可以增强配备Sigmoid Link函数的任何添加二进制分类模型。此外，它们为概率空间中模型输出的添加剂组件提供了直接的全球和本地归因。我们认为LAM和SubscaleHedge提高了其基本算法的解释性。在广泛的财务建模数据中，使用严格的无效 - 假设测试测试，我们表明我们的算法在ROC-AUC和校准方面不会受到巨大的绩效惩罚。
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called subscales. LAMs can augment any additive binary classification model equipped with a sigmoid link function. Moreover, they afford direct global and local attributions of additive components to the model output in probability space. We argue that LAMs and SubscaleHedge improve the interpretability of their base algorithms. Using rigorous null-hypothesis significance testing on a broad suite of financial modelling data, we show that our algorithms do not suffer from large performance penalties in terms of ROC-AUC and calibration.
