SHAPExplain machine learning models with Shapley values
SHAP (SHapley Additive exPlanations) provides a framework to understand the output of machine learning models using Shapley values. Key features include:
- Explain Any Model: Applicable to any machine learning model to interpret predictions.
- Fair Attribution: Ensures fair distribution of credits among features based on their contributions.
- Versatile Use Cases: Useful for analyzing image classifications, text analysis, and more.
Practical Use Cases:
- Interpret model predictions for better insights.
- Explain fairness in AI decisions.
- Enhance trust in model results by providing transparent explanations.
