Tree-based local explanations of machine learning model predictions, AraucanaXAI
This addresses the need for interpretability in high-stakes applications like medicine, where understanding model predictions is crucial, though it appears incremental as it builds on existing XAI approaches.
The authors tackled the problem of explaining predictions from complex machine learning models by proposing a novel method that generates local explanations for both classification and regression tasks, achieving improved fidelity to the original model and handling non-linear decision boundaries.
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 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