🔅 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.

Lime client built using flutter

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

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

ProjectFNF is a mostly quality-of-life engine for Friday Night Funkin. It is easy to understand and is super flexible.

Poco M3/Redmi 9T Global Kernel

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

C# LIME protocol implementation

Simple C# Keylogger (Keyboard Layout)

Build an scikit-learn model to predict churn using customer telco data.

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

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

A port of Friday Night Funkin' v0.2.8 made by rebuilding the code via reverse engineering.

This repository will have all my gnuradio exsamples

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

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

Short overview over the components used by Lime Scooters fleet

Overview of different model interpretability libraries.

Local explanations with uncertainty 💐!