Contextual Decision Trees
This work addresses the need for interpretable machine learning models in domains requiring transparency, though it is incremental as it builds on existing Random Forest methods.
The paper tackles the problem of improving interpretability in Random Forests by proposing a contextual bandit framework that dynamically selects a single shallow tree from the ensemble for predictions, achieving performance comparable to the full Random Forest and superior to a standalone CART tree.
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.