dianna-ai/dianna

👋 Xplique is a Neural Networks Explainability Toolbox

Interpretability and explainability of data and machine learning models

A game theoretic approach to explain the output of any machine learning model.

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

Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world datasets and workflows.

Lime: Explaining the predictions of any machine learning classifier

Human-explainable AI.

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.