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.

翻译：以随机森林为重点,我们提出了一个多武装背景强盗建议框架,用于根据地貌选择一个有学识的组合的浅树。在随机森林之上工作的经过培训的系统动态地确定了一个负责提供最终产出的基础预测器。这样,我们通过遵守推荐树的规则获得当地的解释。所进行的实验表明,我们的动态方法优于一个独立安装的CART决策树,在预测性能方面可以与整个黑盒随机森林相比。