Lime Open Source Projects

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

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Python implementation of two low-light image enhancement techniques via illumination map estimation

Lime client built using flutter

Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

C# LIME protocol implementation