Tree-based local explanations of machine learning model predictions, AraucanaXAI [article]

Enea Parimbelli, Giovanna Nicora, Szymon Wilk, Wojtek Michalowski, Riccardo Bellazzi
2021 arXiv   pre-print
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. In the present article we 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
more » ... e 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
arXiv:2110.08272v1 fatcat:njki4h3qxvf4hbo3olfj54snmi