Project Title

shap — Game Theoretic Explanations for Machine Learning Models

Overview

SHAP (SHapley Additive exPlanations) is an open-source Python library that uses game theory to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory, providing a unified approach to model interpretability. This library stands out for its ability to offer consistent and locally accurate additive feature attribution methods based on expectations.

Key Features

Unified approach to explain any machine learning model's output
High-speed exact algorithm for tree ensemble methods
Supports various models including XGBoost, LightGBM, CatBoost, scikit-learn, and pyspark
Visualizes explanations through waterfall plots, force plots, and summary plots

Use Cases

Data scientists needing to understand model predictions for better decision-making
Model developers looking to debug and improve their machine learning models
Compliance officers ensuring models meet regulatory requirements for explainability

Advantages

Provides a game-theoretic approach to model interpretability
Offers high-speed algorithms for tree ensemble methods
Supports a wide range of popular machine learning models
Includes various visualization tools for easy understanding of model behavior

Limitations / Considerations

The library may have a steeper learning curve for those unfamiliar with game theory concepts
Performance may vary depending on the complexity and type of machine learning model used
The effectiveness of explanations can be model-dependent

LIME: A library that provides local interpretable model-agnostic explanations, differing from SHAP in its approach to local explanations.
ELI5: A Python package that provides simple, human-readable explanations of machine learning models, with a focus on feature importance.
InterpretML: A tool for explaining the predictions of any machine learning model, offering a different set of algorithms and visualizations compared to SHAP.

Basic Information

GitHub: https://github.com/shap/shap
Stars: 24,273
License: Unknown
Last Commit: 2025-08-20

📊 Project Information

Project Name: shap
GitHub URL: https://github.com/shap/shap
Programming Language: Jupyter Notebook
⭐ Stars: 24,273
🍴 Forks: 3,413
📅 Created: 2016-11-22
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

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