Lime Python Packages

shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

xwhy

Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations

covidxai

Implementation of explainability algorithms (layer-wise relevance propagation, local interpretable model-agnostic explanations, gradient-weighted class activation mapping) on computer vision architectures to identify and explain regions of COVID 19 pneumonia in chest X-ray and CT scans.

visualime

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

impl

LIME-based library for interpretation of local models

aizenx-xai

AizenX is an open-source Explainable AI (XAI) framework for model interpretation, counterfactual analysis, bias detection, and visualization.

pyinterpret

A unified Python library for machine learning model interpretation (SHAP, LIME, permutation importance, partial dependence)

xfin-xai

Privacy-Preserving Explainable AI Library for Financial Services and Banking Systems

tic

TIC is a library that acts as a Toolbox for Interpretability Comparison.

lime-stratified

[AAAI-24 Paper] Using Stratified Sampling to improve LIME image explanations