Model-Interpretation

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

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

Pytorch Implementation of recent visual attribution methods for model interpretability

Collection of NLP model explanations and accompanying analysis tools

Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

Simple C# Keylogger (Keyboard Layout)

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN