BellatrExplorer: An Interactive Random Forest Local Explainability Dashboard

Abstract

This paper presents BellatrExplorer, a dashboard application to interactively explore random forest predictions on the individual instance level. The application is inspired by the recently proposed local interpretability toolbox Bellatrex, that exploits the internal random forest structure to extract 1-3 prototype rules that act as a surrogate model for an instance of interest. BellatrExplorer is aimed at expert users trying to better understand the behavior of their random forest in a specific application, and could allow to uncover potential biases or artifacts arising in model training. Currently, the tool supports random forests for binary classification, regression, and survival analysis tasks. It features (1) intuitive exploration of univariate predictive counterfactuals, (2) analysis of decision tree rules to the individual split level, and (3) a visualisation of the rules extracted by Bellatrex that allow to assess the local interpretation at a glance. The tool is available at https://github.com/robbedhondt/BellatrExplorer/ and a demonstration video can be found at https://itec.kuleuven-kulak.be/bellatrexplorer/