Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear 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) A 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 retaining local faithfulness to the original model. These tools enable us to (1) identify high-magnitude but low-frequency nonlinear mortality risk factors in the US population, (2) highlight distinct population subgroups with shared risk characteristics, (3) identify nonlinear interaction effects among risk factors for chronic kidney disease and (4) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model’s performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains.
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The pre-processed mortality data are available at http://github.com/suinleelab/treexplainer-study. Privacy restrictions prevent the release of the hospital procedure-related data, and the kidney disease data are only available directly from the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK).
Code supporting this paper is published online at https://github.com/suinleelab/treexplainer-study. A widely used Python implementation of TreeExplainer is available at https://github.com/slundberg/shap, and portions of it are included in the standard release of XGBoost (https://xgboost.ai), LightGBM (https://github.com/Microsoft/LightGBM) and CatBoost (https://catboost.ai).
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We are grateful to R. Chen, A. Okeson, C. Robinson, V. Khotilovich, N. Hiranuma, J. Janizek, M. T. Ribeiro, J. Schreiber, P. Hall and members of S.-I.L.’s group for the feedback and assistance they provided during the development and preparation of this research. This work was funded by the National Science Foundation (DBI-1759487, DBI-1552309, DBI-1355899, DGE-1762114 and DGE-1256082), American Cancer Society (127332-RSG-15-097-01-TBG), National Institutes of Health (R35 GM 128638 and R01 NIA AG 061132), and an unrestricted gift from the Northwest Kidney Centers to the University of Washington Kidney Research Institute. The Chronic Renal Insufficiency Cohort (CRIC) study was conducted by the CRIC investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The data from the CRIC study reported here were supplied by the NIDDK Central Repositories. This manuscript was not prepared in collaboration with Investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories or the NIDDK.
The authors declare no competing interests.
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Lundberg, S.M., Erion, G., Chen, H. et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2, 56–67 (2020). https://doi.org/10.1038/s42256-019-0138-9
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