Auracana XAI

Tree-based local explanations of machine learning model predictions. Implementation of the pipeline described in Parimbelli et al., 2021

About The Project

Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between performance and intelligibility is often to be faced, especially in high-stakes applications like medicine. This project propose a novel methodological approach for generating explanations of the predictions of a generic ML model, given a specific instance for which the prediction has been made, that can tackle both classification and regression tasks. Advantages of the proposed XAI approach include improved fidelity to the original model, the ability to deal with non-linear decision boundaries, and native support to both classification and regression problems.

Keywords: explainable AI, explanations, local explanation, fidelity, interpretability, transparency, trustworthy AI, black-box, machine learning, feature importance, decision tree, CART, AIM.

Paper

The araucanaxai package implements the pipeline described in Tree-based local explanations of machine learning model predictions - Parimbelli et al., 2021.