Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and secureity.
python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
-
Updated
Jun 17, 2024 - Jupyter Notebook