论文标题
上下文决策树
Contextual Decision Trees
论文作者
论文摘要
为了关注随机森林，我们提出了一个多武器的上下文匪徒推荐框架，用于基于特征的学习合奏的单个浅树的选择。在随机森林之上起作用的训练有素的系统动态识别了负责提供最终输出的基础预测因子。这样，我们通过观察推荐树的规则来获得本地解释。进行的实验表明，我们的动态方法优于独立的卡车决策树，并且在预测性能方面与整个黑盒随机森林相当。
Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble. The trained system, which works on top of the Random Forest, dynamically identifies a base predictor that is responsible for providing the final output. In this way, we obtain local interpretations by observing the rules of the recommended tree. The carried out experiments reveal that our dynamic method is superior to an independent fitted CART decision tree and comparable to the whole black-box Random Forest in terms of predictive performances.
