论文标题
具有监督的机器学习算法的本地替代模型
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
论文作者
论文摘要
近年来，由于其优于传统统计方法的优越的预测性能，近年来，监督的机器学习（SML）算法（例如梯度增强，随机森林和神经网络）已变得流行。但是，它们的复杂性使结果很难解释，而没有其他工具。在开发用于解释SML模型的全球和本地诊断方面，最近有很多工作。在本文中，我们提出了一个局部解释的模型，该模型采用拟合的ML响应表面，使用基于模型的回归树对预测器空间进行分区，并拟合每个节点上可解释的主效应模型。我们适应该算法在处理高维预测因子方面有效。尽管主要重点是解释性，但由此产生的替代模型也具有相当良好的预测性能。
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods. However, their complexity makes the results hard to interpret without additional tools. There has been a lot of recent work in developing global and local diagnostics for interpreting SML models. In this paper, we propose a locally-interpretable model that takes the fitted ML response surface, partitions the predictor space using model-based regression trees, and fits interpretable main-effects models at each of the nodes. We adapt the algorithm to be efficient in dealing with high-dimensional predictors. While the main focus is on interpretability, the resulting surrogate model also has reasonably good predictive performance.
