SHAP
The explainability library that attributes every prediction to its features with Shapley values.
SHAP computes, for each prediction, how much every feature contributed, using Shapley values from game theory: the contributions add up exactly to the model's output. TreeExplainer derives them exactly and quickly for XGBoost, LightGBM, CatBoost and random forests; KernelExplainer and DeepExplainer cover any black-box model or neural network. Its plots (waterfall, beeswarm, dependence) make explanations readable by non-specialists, and it's our choice whenever a model has to be audited, justified to business stakeholders or compliant with a regulatory requirement.
What SHAP brings to your project.
Typical use cases: Explaining credit scores, model audits, regulatory compliance, feature debugging.
- 01
Additive Shapley values: local contributions consistent with the prediction.
- 02
TreeExplainer: exact, fast computation for XGBoost, LightGBM, CatBoost, random forests.
- 03
KernelExplainer and DeepExplainer for any black-box model or deep network.
- 04
Waterfall, beeswarm and dependence plots: from local explanation to global importance.
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Entrust your project
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A project with SHAP?
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