Rule Learning Python Packages

imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

mlrl-common

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-boomer

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-seco

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

xcsf

XCSF learning classifier system: rule-based online evolutionary machine learning

c50py

C5.0 decision trees and rulesets for Python: native categorical splits, missing values, boosting and rules exported to SQL, as scikit-learn estimators

mlrl-testbed

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-util

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

te2rules

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

mlrl-testbed-sklearn

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-testbed-arff

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-testbed-slurm

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

rulechef

Learn rule-based models from examples using LLM-powered synthesis. Replace expensive LLM calls with fast, deterministic, inspectable regex, code, or spaCy rules.

pyalcs

Implementation of Anticipatory Learning Classifiers System (ALCS) in Python