Hybrid feature tweaking: Combining random forest similarity tweaking with CLPFD

Abstract

When using prediction models created from data, it is in certain cases not sufficient for the users to only get a prediction, sometimes accompanied with a probability of the predictive outcome. Instead, a more elaborate answer is required, like given the predictive outcome, how can this outcome be changed to a wished outcome, i.e., feature tweaking. In this paper we introduce a novel hybrid method for performing feature tweaking that builds upon Random Forest Similarity Tweaking and utilizing a Constraint Logic Programming solver for the Finite Domain (CLPFD). This hybrid method is compared to only using a CLPFD solver and to using a previously known feature tweaking algorithm, Actionable Feature Tweaking. The results show that the hybrid method provides a good balance between the distances, comparing the original example and the tweaked example, and completeness, the number of successfully tweaked examples, compared to the other methods. Another benefit with the novel method, is that the user can specify a prediction threshold for feature tweaking and adjust weights of features to mimic the real-world cost of changing feature values.