Interpretable Machine Learning Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

interpret

Fit interpretable models. Explain blackbox machine learning.

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

dalex

moDel Agnostic Language for Exploration and eXplanation

shapiq

Shapley Interactions and Shapley Values for Machine Learning

piml

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

pyaerial

Scalable association rule mining from tabular datasets.

xaisuite

XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.

pytorch-concepts

PyC (Pytorch Concepts) is a PyTorch-based library for training concept-based interpretable deep learning models.

snore-embedding

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

torch-explain

PyTorch Explain: Interpretable Deep Learning in Python.

lirisimpact

IMPACT framework, an interpretable multi-target framework for ordinal outputs

omnixai

OmniXAI: A Library for eXplainable AI

pyss3

A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:

featuremap

FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.

edge2torch

Build sparsely connected PyTorch neural networks from prior-knowledge graphs, with optional feature- and node-level attribution.

explainx

Explain & debug any blackbox machine learning model with a single line of code.

midlearn

A scikit-learn compatible Python wrapper of the midr R package

zennit-crp

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

powerlift

Fit interpretable models. Explain blackbox machine learning.

pytsetlinmachineparallel

Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.

xpotato

XAI based human-in-the-loop framework for automatic rule-learning.

visualime

Implementation of LIME focused on producing user-centric local explanations for image classifiers.