shap and streamlit-shap
The streamlit-shap wrapper is a complementary frontend integration tool that enables visualization of SHAP's game-theoretic explanations within Streamlit applications, making them ecosystem complements rather than competitors.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
This tool helps data scientists and machine learning engineers understand why their machine learning models make specific predictions. By taking a trained model and input data, it shows how much each individual feature contributes to the final output, clarifying complex model behavior. It's designed for anyone building or using ML models who needs to explain their results, like a business analyst evaluating a credit risk model or a medical researcher interpreting a diagnostic tool.
About streamlit-shap
snehankekre/streamlit-shap
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
This is a tool for Python developers who build data applications with Streamlit and need to explain how their machine learning models make predictions. It takes SHAP (SHapley Additive exPlanations) plots generated from a model's output and displays them directly within a Streamlit application. Data scientists and ML engineers can use this to create interactive dashboards that clarify model behavior for stakeholders.
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