linkedin / TE2Rules

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

Date Created 2022-10-31 (2 years ago)
Commits 129 (last one 10 months ago)
Stargazers 50 (0 this week)
Watchers 7 (0 this week)
Forks 5
License other
Ranking

RepositoryStats indexes 618,350 repositories, of these linkedin/TE2Rules is ranked #482,836 (22nd percentile) for total stargazers, and #274,131 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #93,545/125,168.

linkedin/TE2Rules is also tagged with popular topics, for these it's ranked: machine-learning (#6,880/8271),  explainable-ai (#124/176),  interpretability (#131/173)

Other Information

linkedin/TE2Rules has Github issues enabled, there are 3 open issues and 7 closed issues.

There have been 3 releases, the latest one was published on 2024-04-22 (10 months ago) with the name v1.0.0.

Star History

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Recent Commit History

129 commits on the default branch (main) since jan '22

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Issue History

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Languages

The primary language is Python but there's also others...

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updated: 2025-02-21 @ 11:12am, id: 560166856 / R_kgDOIWN3yA