Local Sensitivity in Decision Tree Ensembles with Applications to Explainability and Monitoring

Decision Tree Ensembles (DTE) are a popular model that are widely used in various sectors, such as, finance and health. The popularity of this model is essentially due to their inherent *explainability*. It is much easier to understand how a DTE is making decisions compared to most other ML models. We study the problem of how sensitive the prediction of a DTE is, with respect to a subset of its features. This problem has been studied in a *global* setting, where the objective is to find two data points that agree on a set of features, but have different classifications. In this article, we *local*-ize this problem. *Given a data point*, we check if it is possible to perturb this point to get another point having a different classification. We establish the precise computational hardness of deciding this problem. We then show that two well-studied problems, *explainability* and *monitorability* for DTEs, reduce to checking *local sensitivity*. We propose an algorithm for solving local sensitivity, thereby also solving explainability and monitorability. Our prototype implementation demonstrates our approach is competitive with respect to their dedicated tools.