Tree-Ensemble Repair through Constrained Optimization

It is widely recognized that in many applications, strong predictive performance is not enough to warrant use of a learned model. Additionally, it is required to ascertain whether the model conforms to certain properties such as fairness, robustness or safety. Unfortunately, most learning approaches are not guaranteed to produce a model that adheres to these properties. Model repair addresses this through a post-hoc debugging loop. Approaches such as verification are used to check if the desired property holds for a model; if not they return a counterexample which is used to modify or repair the ML model. We propose a novel repair method for tree ensembles. Our approach is based on a MILP encoding where the objective is to modify the leaf values in the tree ensemble such that it fixes the counterexamples while at the same time preserves predictive performance. The use of constrained optimization over the model parameters is compelling as it is guaranteed to fix a given set of counterexamples. We implement and evaluate our method in two use-case scenarios, classification requiring fairness and action policies in MDPs requiring safety. Results show that our MILP repair of tree ensembles can be feasible computationally, and is often successful in improving the desired property while not degrading performance.