UrbsLab

The Heuristic Evolutionary Rule Optimization System (HEROS) is a supervised rule-based machine learning algorithm designed to agnostically model diverse structured data problems and yield compact human interpretable solutions. This implementation is scikit-learn compatible.

Source code for the Genetic Architecture Model Emulator for Testing and Evaluating Software (GAMETES) is an algorithm for the generation of complex single nucleotide polymorphism (SNP) models for simulated association studies.

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data

scikit-FIBERS (Feature Inclusion Bin Evolver for Risk Stratification) is a scikit-learn compatible machine learning algorithm for modeling or feature learning in survival analyses where feature 'burden' may be predictive of risk strata. Originally designed to identify amino-acid positions where mismatch burden predicts kidney graft failure risk.

Educational demos for building a simple expert system using Python

LCS Discovery and Visualization Environment (LCS-DIVE)

A scikit-learn implementation based on ExSTraCS 2.0

A scikit-learn-compatible Python implementation of eLCS, a supervised learning variant of Learning Classifier Systems