lime
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Implementation of LIME focused on producing user-centric local explanations for image classifiers.
Using XAI algorithms on Computer Vision models to explain predictions.
AizenX - Explainable AI toolkit for model interpretability and fairness
Privacy-Preserving Explainable AI Library for Financial Services and Banking Systems
LIME-based library for interpretation of local models
TIC is a library that acts as a Toolbox for Interpretability Comparison.
Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations
