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

Jupyter Notebook 99.67% Dockerfile 0.02% Python 0.31%
accountability data-mining data-science decision-tree fairness fatml gradient-boosting-machine h2o iml interpretability interpretable interpretable-ai interpretable-machine-learning interpretable-ml lime machine-learning machine-learning-interpretability python transparency xai

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interpretable_machine_learning_with_python's Issues

Issue with Graphviz

Hey,

Firstly, this is an amazing work of yours. I really admire what you have done. I am facing a small issue while displaying the png file. I cannot convert it from dot to png. Can you please help me with this?

Add adverse impact ratio for dia.ipynb

We need to add another metric:

Adverse Impact: (tp + fp) / (tp + fp + tn + fn)

Adverse Impact Disparity (Ratio): non-reference adverse impact / reference adverse impact

Adverse Impact Parity: low_threshold < Adverse Impact Disparity < high_threshold

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