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interpretable_machine_learning_with_python
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.
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Jupyter Notebook
Updated: 1mo ago
📋 Project at a Glance
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What's this?Security & Governance project leveraging accountability and data-mining, built with Jupyter Notebook, open-source
Who made it?Maintained by jphall663 team, 682⭐ on GitHub, #164 out of 413 in Security & Governance
Why does it exist?As the Security & Governance landscape evolved, the jphall663 team identified the need for better accountability solutions. interpretable_machine_learning_with_python was created to simplify data-mining workflows.
What can it do?Key use cases: data-science, decision-tree, fairness
How to install with AI?Use an AI coding assistant to follow the README and automatically handle the install and environment setup.
🔗 github.com/jphall663/interpretable_machine_learning_with_python
🔗 github.com/jphall663/interpretable_machine_learning_with_python
Topics
accountabilitydata-miningdata-sciencedecision-treefairnessfatmlgradient-boosting-machineh2oimlinterpretabilityinterpretableinterpretable-aiinterpretable-machine-learninginterpretable-mllimemachine-learningmachine-learning-interpretabilitypythontransparencyxai