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

Multicycles.org aggregates on one map, more than 100 share vehicles like bikes, scooters, mopeds and cars. Demo APP for the Data Flow API, see https://flow.fluctuo.com

C# LIME protocol implementation

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

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

Simple C# Keylogger (Keyboard Layout)

A library for drawing graphics on the console screen

Short overview over the components used by Lime Scooters fleet

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

2D OpenGL Render Library in haxe/lime - old origin version

FastAI Model Interpretation with LIME

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

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).

An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learning algorithm - in this case a Radom Forest regressor.

Gets a list of closest lime scooters to a location, or notifies you if one is close

Segway Ninebot serial communication analyzer (ESCx such as Voi, Circ / Flash, Bolt, Dott, Jump, Tier...)

General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems

Using LIME (Local Interpretable Model-Agnostic Explanations) for text data

Benchmark scenes for OpenFL

This repository will have all my gnuradio exsamples

dragdrop in haxe lime

Interpretability of Image Keras Models

Explaining complex ML models

Interpreting Machine Learning models

A comprehensive Churn Classification solution aimed at laying out the steps of a classification solution, including EDA, Stratified train test split, Training multiple classifiers, Evaluating trained classifiers, Hyperparameter tuning, Optimal probability threshold tuning, model comparison, model selection and Whiteboxing models for business sense. (Python)

Compare traditional neural networks with self explaining neural networks in terms of performance and interpretability.

Overview of different model interpretability libraries.

LIME.JS 配套的项目管理工具