linkedin / TE2Rules

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

Date Created 2022-10-31 (about a year ago)

Commits 129 (last one 2 months ago)

Stargazers 37 (0 this week)

Watchers 8 (0 this week)

Forks 5

License other

Ranking

RepositoryStats indexes 534,880 repositories, of these linkedin/TE2Rules is ranked #489,346 (9th percentile) for total stargazers, and #236,088 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #93,039/103,597.

linkedin/TE2Rules is also tagged with popular topics, for these it's ranked: machine-learning (#6,965/7374), explainable-ai (#138/154), interpretability (#133/145)

Other Information

linkedin/TE2Rules has Github issues enabled, there is 1 open issue and 6 closed issues.

There have been 3 releases, the latest one was published on 2024-04-22 (2 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

Star History

Github stargazers over time

Watcher History

Github watchers over time, collection started in '23

Recent Commit History

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

Yearly Commits

Commits to the default branch (main) per year

Issue History

Languages

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

updated: 2024-06-21 @ 04:45pm, id: 560166856 / R_kgDOIWN3yA