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SHAP Explainer

This app serves as a starter guide for understanding and explaining regression models using SHAP values. It contains the following sections:

  • Feature Importance (📊): Analyze the significance of different features in the model.
  • Regression Stats (📈): Get statistical summaries and evaluations for the model.
  • Individual Predictions (👥): Generate and view individual predictions.
  • Feature Dependence (🔍): Examine how different features interact within the model.
  • What If (❓): Conduct 'What-If' analyses to understand how changes in feature values could affect predictions.

You can access a live demo of the app here.

Setup

To get started, clone this repository and navigate into the directory.

git clone https://github.com/ahmad-alismail/shap-explainer.git
cd shap-explainer

Install the necessary dependencies with the following command:

pip install -r requirements.txt

Run the app with Streamlit:

streamlit run hello.py

Contributing

Feel free to fork the repository, make changes, and submit pull requests. Feedback is always welcome.

License

MIT License