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

Comprehensive suite for rule-based learning

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

The codes for our ACL'22 paper: PRBOOST: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning.

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

Rule-Guided Graph Neural Networks for Recommender Systems, ISWC 2020

Implementation of Anticipatory Learning Classifiers System (ALCS) in Python

Explain fully connected ReLU neural networks using rules

A Java implementation for LORD, a rule learning algorithm proposed in the article "Efficient learning of large sets of locally optimal classification rules" with the approach of searching for a locally optimal rule for each training example. Machine Learning, volume 112, pages 571–610 (2023)

Implementation of pruning hypothesis space using domain theories -- M. Svatoš, G. Šourek, F. Zeležný, S. Schockaert, and O. Kuželka: Pruning Hypothesis Spaces Using Learned Domain Theories, ILP'17

Documentation of the BOOMER machine learning algorithm.

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

Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20

A rule learning algorithm for the deduction of syndrome definitions from time series data.