https://github.com/csinva/disentangled-attribution-curves

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
https://github.com/csinva/disentangled-attribution-curves

ai artificial-intelligence boosting ensemble-model explainable-ai feature-engineering feature-importance interpretability machine-learning ml python random-forest random-forests scikit-learn statistics stats

Last synced: 14 days ago
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Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

• Host: GitHub
• URL: https://github.com/csinva/disentangled-attribution-curves
• Owner: csinva
• License: mit
• Created: 2018-09-01T02:41:23.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2021-02-11T21:17:52.000Z (over 3 years ago)
• Last Synced: 2024-10-04T13:07:49.465Z (about 1 month ago)
• Topics: ai, artificial-intelligence, boosting, ensemble-model, explainable-ai, feature-engineering, feature-importance, interpretability, machine-learning, ml, python, random-forest, random-forests, scikit-learn, statistics, stats
• Language: Python
• Homepage: https://arxiv.org/abs/1905.07631
• Size: 4.63 MB
• Stars: 26
• Watchers: 7
• Forks: 4
• Open Issues: 1

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