From Local Explanations to Global Understanding with Explainable AI for Trees.

ABSTRACT: Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are popular non-linear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here, we improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retai

SUBMITTER: Lundberg SM 

PROVIDER: S-EPMC7326367 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature