lime

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

An open source library for creative expression on the web, desktop, mobile and consoles. Inspired by the classic Flash and AIR APIs.

A foundational Haxe framework for cross-platform development

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

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

Implementation of the paper, "LIME: Low-Light Image Enhancement via Illumination Map Estimation", which is for my graduation thesis.

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

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

Short overview over the components used by Lime Scooters fleet

Simple C# Keylogger (Keyboard Layout)

This repository will have all my gnuradio exsamples

Overview of different model interpretability libraries.

Local explanations with uncertainty 💐!

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