jphall663/interpretable_machine_learning_with_python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

语言：Jupyter Notebook · GitHub 星标：682 · Fork：206

话题：accountability · data-mining · data-science · decision-tree · fairness · fatml · gradient-boosting-machine · h2o

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