shap
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
What is shap?
SHAP is a Python library that explains the output of any machine learning model using Shapley values from cooperative game theory. It assigns each feature an importance value for a particular prediction, showing how much each feature contributed to pushing the model's output away from the baseline expectation. This helps data scientists and ML engineers understand model behavior, debug predictions, and build trust in AI systems.
Data scientists, machine learning engineers, and researchers who need to interpret and explain complex model predictions, particularly in regulated industries or applications requiring transparency.
SHAP provides mathematically rigorous, model-agnostic explanations with consistent properties, unifying multiple explanation methods into one framework. Its optimized implementations for tree models and deep learning make it practical for real-world use, while rich visualizations make explanations accessible to stakeholders.
Overview
A game theoretic approach to explain the output of any machine learning model.
Use Cases
Best For
- Explaining predictions from black-box models like gradient boosting ensembles
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