shap and shapiq
While SHAP is a mature, general-purpose library for computing Shapley values and SHAP interactions across diverse model types, ShapIQ is a specialized library focused specifically on higher-order Shapley interactions (n-way feature interactions), making them **complements** that users might combine when investigating both individual feature importance and complex multi-feature interaction effects.
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 shapiq
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
This tool helps data scientists and machine learning practitioners understand why their models make certain predictions. You provide a trained machine learning model and your dataset, and it shows you how individual features and combinations of features contribute to a specific prediction. This goes beyond just knowing which features are important, revealing how they interact with each other.
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