csinva / imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Why this job: matched by what the repo talks about: compatible, model, package
What it does
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 6 days ago
- Stars
- 1,630
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v3.0.4
- Released 37 days ago
- Built with
- Jupyter Notebook
- 61% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 95% closed
- 5 still open
- Pull requests
- 205 merged
- 8 waiting
- Releases
- 42
- 5 in the last year
- Contributing guide (opens in new tab)
- No security policy
- No code of conduct
More: languages, topics, project record and preview image
Languages
- Jupyter Notebook 61%
- Python 38%
- JavaScript 1%
- CSS <1%
Topics
- interpretability
- machine-learning
- data-science
- artificial-intelligence
- ml
- ai
- statistics
- scikit-learn
- python
- optimal-classification-tree
- rulefit
- imodels
Project record
- JobModel gateways
- StatusListed
- Commits1,488
- Forks150
- Watching19
- Stars in 28 days+4
- CreatedJul 4, 2019
- First seen by usNov 7, 2026
- CheckedNov 8, 2026