论文标题
可解释的机器学习与梯度提升机的合奏
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
论文作者
论文摘要
提出了一种基于众所周知的广义添加剂模型的黑框模型的局部和全局解释的方法。可以将其视为使用神经添加剂模型对算法的扩展或修改。该方法基于使用梯度提升机（GBM）的集合，以便在单个特征上学习每个GBM并产生该功能的形状函数。该集合作为单独的GBM的加权总和组成，导致形状构成广义添加剂模型的形状函数的加权总和。 GBM是使用深度1的随机决策树并行构建的，该决策树提供了非常简单的体系结构。使用套索方法计算在每次提升的迭代中计算GBM的权重以及功能，然后通过特定的平滑过程进行更新。与神经添加剂模型相比，该方法提供了明确形式的特征权重，并且经过训练。许多用于在合成和实际数据集上实现拟议方法的算法的数值实验证明了其局部和全局解释的效率和属性。
A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on a single feature and produces a shape function of the feature. The ensemble is composed as a weighted sum of separate GBMs resulting a weighted sum of shape functions which form the generalized additive model. GBMs are built in parallel using randomized decision trees of depth 1, which provide a very simple architecture. Weights of GBMs as well as features are computed in each iteration of boosting by using the Lasso method and then updated by means of a specific smoothing procedure. In contrast to the neural additive model, the method provides weights of features in the explicit form, and it is simply trained. A lot of numerical experiments with an algorithm implementing the proposed method on synthetic and real datasets demonstrate its efficiency and properties for local and global interpretation.
