论文标题
决策树的混合物，用于可解释的机器学习
Mixture of Decision Trees for Interpretable Machine Learning
论文作者
论文摘要
这项工作介绍了一种新型的可解释的机器学习方法，称为决策树的混合物（MODT）。它构成了专家集成体系结构的混合的特殊情况，该架构将线性模型用作门控功能和决策树作为专家。我们提出的方法理想地适合于无法通过单个决策树令人满意地学习的问题，但可以将其分为子问题。然后，每个子问题都可以从单个决策树中得到很好的学习。因此，MODT可以被视为一种方法，可以通过使其每个决策都可以理解和可追溯到人类来提高性能，同时保持可解释性。 我们的工作伴随着Python的实现，该实现使用了可解释的门控函数，快速学习算法以及直接接口来进行微调可解释的可视化方法。实验证实了实施作用，更重要的是，与单个决策树和相似复杂性的随机森林相比，我们方法的优越性。
This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a single decision tree, but which can alternatively be divided into subproblems. Each subproblem can then be learned well from a single decision tree. Therefore, MoDT can be considered as a method that improves performance while maintaining interpretability by making each of its decisions understandable and traceable to humans. Our work is accompanied by a Python implementation, which uses an interpretable gating function, a fast learning algorithm, and a direct interface to fine-tuned interpretable visualization methods. The experiments confirm that the implementation works and, more importantly, show the superiority of our approach compared to single decision trees and random forests of similar complexity.
