  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.
