Extracting optimal actionable plans from additive tree models

Although amazing progress has been made in machine learning to achieve high generalization accuracy and efficiency, there is still very limited work on deriving meaningful decision-making actions from the resulting models. However, in many applications such as advertisement, recommendation systems, social networks, customer relationship management, and clinical prediction, the users need not only accurate prediction, but also suggestions on actions to achieve a desirable goal (e.g., high ads hit rates) or avert an undesirable predicted result (e.g., clinical deterioration). Existing works for extracting such actionability are few and limited to simple models such as a decision tree. The dilemma is that those models with high accuracy are often more complex and harder to extract actionability from. In this paper, we propose an effective method to extract actionable knowledge from additive tree models (ATMs), one of the most widely used and best off-the-shelf classifiers. We rigorously formulate the optimal actionable planning (OAP) problem for a given ATM, which is to extract an actionable plan for a given input so that it can achieve a desirable output while maximizing the net profit. Based on a state space graph formulation, we first propose an optimal heuristic search method which intends to find an optimal solution. Then, we also present a sub-optimal heuristic search with an admissible and consistent heuristic function which can remarkably improve the efficiency of the algorithm. Our experimental results demonstrate the effectiveness and efficiency of the proposed algorithms on several real datasets in the application domain of personal credit and banking.