🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
lime 开源项目推荐(723 个)
lime 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 723 个仓库一个用于在网络、桌面、移动设备和游戏机上进行创意表达的开源库。灵感来自经典的Flash和Air API。
A foundational Haxe framework for cross-platform development
用于训练可解释机器学习模型、解释机器学习模型,以及调试机器学习模型以提升准确性、减少歧视并增强安全性的技术示例。
Python implementation of two low-light image enhancement techniques via illumination map estimation
Lime client built using flutter
Qt-DAB,一种通用软件DAB(DAB+)解码器,侧重于显示信号
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
Implementation of the paper, "LIME: Low-Light Image Enhancement via Illumination Map Estimation", which is for my graduation thesis.
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)
Short overview over the components used by Lime Scooters fleet
ProjectFNF is a mostly quality-of-life engine for Friday Night Funkin. It is easy to understand and is super flexible.
解包、打包并重新签名,使用第一版“Lime”加密文件。
Simple C# Keylogger (Keyboard Layout)
这个仓库会有我所有的gnuradio样本
Overview of different model interpretability libraries.
A port of Friday Night Funkin' v0.2.8 made by rebuilding the code via reverse engineering.
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/lime。
- 该话题在 GitHub 上共有 723 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 9530 Star,覆盖 8 种主要语言,数据更新于 2026-11-07。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。