From local explanations to global understanding with explainable AI for trees

Author:

Lundberg Scott M.,Erion GabrielORCID,Chen Hugh,DeGrave Alex,Prutkin Jordan M.,Nair Bala,Katz Ronit,Himmelfarb Jonathan,Bansal Nisha,Lee Su-InORCID

Funder

National Science Foundation

American Cancer Society

U.S. Department of Health & Human Services | National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference41 articles.

1. The state of data science & maching learning. Kaggle https://www.kaggle.com/surveys/2017 (2017).

2. Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer Series in Statistics, Springer, 2001).

3. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4768–4777 (2017).

4. Saabas, A. treeinterpreter python package. GitHub https://github.com/andosa/treeinterpreter (2019).

5. Ribeiro, M. T., Singh, S. & Guestrin, C. Why should i trust you?: Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).

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