Project Title
lime — Explaining Machine Learning Predictions with Local Interpretable Model-Agnostic Explanations
Overview
Lime is an open-source JavaScript library designed to explain the predictions of any machine learning classifier. It provides local interpretable model-agnostic explanations, enabling users to understand individual predictions for text classifiers, table-based classifiers, and image classifiers. Lime is built on the principles outlined in a research paper and offers a range of tutorials and API documentation to facilitate its use.
Key Features
- Local interpretable model-agnostic explanations for any black box classifier
- Support for text, table, and image classifiers
- Built-in support for scikit-learn classifiers
- Extensive tutorials and API documentation
Use Cases
- Data scientists and machine learning engineers needing to understand and explain model predictions
- Researchers analyzing the behavior of machine learning models
- Developers integrating model explanations into applications for better user understanding
Advantages
- Works with any classifier that outputs class probabilities
- Provides clear, visual explanations that can be embedded in iPython notebooks
- Supports a variety of data types, including text, numerical, and categorical data
Limitations / Considerations
- Requires the classifier to implement a function that takes in raw data and outputs class probabilities
- May have limitations in explaining complex or highly nonlinear models
Similar / Related Projects
- ELI5: A Python library for explaining machine learning classifiers, with a focus on scikit-learn models.
- SHAP: A game theoretic approach to explain the output of any machine learning model.
Basic Information
- GitHub: https://github.com/marcotcr/lime
- Stars: 11,991
- License: Unknown
- Last Commit: 2025-09-18
📊 Project Information
- Project Name: lime
- GitHub URL: https://github.com/marcotcr/lime
- Programming Language: JavaScript
- ⭐ Stars: 11,991
- 🍴 Forks: 1,848
- 📅 Created: 2016-03-15
- 🔄 Last Updated: 2025-09-18
🏷️ Project Topics
Topics: [, ]
🔗 Related Resource Links
📚 Documentation
- twoclass
- multiclass
- tabular
- Latin Hypercube Sampling
- Images - Faces
- Images with Keras
- MNIST with random forests
- Images with PyTorch
- Submodular Pick
- here
- here
🌐 Related Websites
This article is automatically generated by AI based on GitHub project information and README content analysis