Fit interpretable models. Explain blackbox machine learning.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems

📜 [NeurIPS 2022] "Symbolic Distillation for Learned TCP Congestion Control", S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang

counterfactual explanations for XGBoost and tree ensemble models - counterfactual reasoning - model interpretability

Interpretable Error Function learning

Implementation of "Toward an Interpretable Alzheimer's Disease Diagnostic Model with Regional Abnormality Representation via Deep Learning"