sametcopur/ruleopt
Optimization-Based Rule Learning for Classification
This tool helps data scientists and machine learning engineers create classification models that are easy to understand and explain. It takes in your dataset and, instead of a complex black-box model, outputs a set of simple, human-readable 'if-then' rules. This is ideal for situations where you need to not only make predictions but also clearly communicate why those predictions are made.
No commits in the last 6 months. Available on PyPI.
Use this if you need to build a classification model where transparency and the ability to explain decisions are as important as prediction accuracy.
Not ideal if your primary concern is achieving the absolute highest predictive accuracy without any need for model interpretability.
Stars
49
Forks
1
Language
Python
License
BSD-3-Clause
Category
Last pushed
Oct 03, 2025
Commits (30d)
0
Dependencies
4
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sametcopur/ruleopt"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of...
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research...
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
cdt15/lingam
Python package for causal discovery based on LiNGAM.
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python