Interpretable Ml

aerosolveScala

A machine learning library designed for human interpretability, featuring debuggable models and a feature transform language.

imodelsJupyter Notebook

A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.

pyGAMPython

A Python library for building Generalized Additive Models (GAMs) with a scikit-learn-like API, emphasizing interpretability and performance.

Auto_ViMLPython

Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.

LatticePython

A TensorFlow library implementing constrained and interpretable lattice-based models with shape constraints like monotonicity and convexity.

sklearn-expertsysPython

A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.

ChefBoostPython

A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.

RuleFitPython

Python implementation of the RuleFit algorithm for interpretable machine learning predictions using rule ensembles.

PySS3Python

A Python library for interpretable text classification using the SS3 model, with built-in visualization tools for explainable AI.

XAI

A curated collection of papers, methods, critiques, and resources for Explainable AI (XAI) and Interpretable Machine Learning.