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imodels

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

mlrl-boomer

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

mlrl-common

Provides common modules to be used by different types of multi-label rule learning algorithms

mlrl-seco

A scikit-learn implementation of a Separate-and-Conquer (SeCo) multi-label rule learning algorithm

xcsf

XCSF learning classifier system: rule-based evolutionary machine learning

te2rules

Python Library to explain tree ensembles using rules

mlrl-util

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

mlrl-testbed

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-sklearn

Adds support for the scikit-learn framework to the package "mlrl-testbed"

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