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Unified approach to explaining ML model output using Shapley values from game theory for feature importance.
Pricing
Free / Open Source
Founded
2017
Team size
1-10 employees
Headquarters
Seattle, WA
At a glance
Best for
Data Scientist, ML Engineer
Pricing model
Free
Try before you buy
Integrates with
Tree Based ModelsLinear modelsNeural networksText modelsImage models+1
About SHAP
SHAP (SHapley Additive exPlanations) is an open-source Python library for explaining the output of machine-learning models using game-theoretic Shapley values. It produces per-feature attributions and visualizations that show how each input drives a prediction, supporting tree ensembles, deep learning, and arbitrary models. It is used by data scientists and ML engineers for model interpretability and debugging.
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Pricing
Commercial model
Pricing model: Free
Free trial: Yes
Free plan: Yes
Contract minimum: Not specified
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SHAP
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