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 8 months ago)

Stargazers 47 (0 this week)

Watchers 8 (0 this week)

Forks 4

License other

Ranking

RepositoryStats indexes 595,856 repositories, of these linkedin/TE2Rules is ranked #485,516 (19th percentile) for total stargazers, and #246,776 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #93,627/119,431.

linkedin/TE2Rules is also tagged with popular topics, for these it's ranked: machine-learning (#6,944/8063), explainable-ai (#128/171), interpretability (#131/169)

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 (8 months ago) with the name v1.0.0.

All Topics

xgboost random-forest rule-learning explainability explainable-ai tree-ensembles interpretability interpretable-ai machine-learning

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Recent Commit History

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

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

updated: 2024-12-20 @ 06:25am, id: 560166856 / R_kgDOIWN3yA