lime topic
interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
multicycles
Multicycles.org aggregates on one map, more than 300 share vehicles like bikes, scooters, mopeds and cars. Demo APP for the Data Flow API, see https://flow.fluctuo.com
Low-light-Image-Enhancement
Python implementation of two low-light image enhancement techniques via illumination map estimation
InterpretDL
InterpretDL: Interpretation of Deep Learning Models,基于『飞桨』的模型可解释性算法库。
shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
lime-flutter
Lime client built using flutter
Lime-For-Time
Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
lime-csharp
C# LIME protocol implementation
Fooling-LIME-SHAP
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)