Lime Python Packages

shapash

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

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.

aizenx-xai

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

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.

impl

LIME-based library for interpretation of local models

xwhy

Explain Why (XWhy) with Statistical Model-agnostic Interpretability with Local Explanations (SMILE)