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02_SHAP_and_Shapley_Values

Machine learning interpretabilityMliForce plotShapleyShap
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k10shetty1 profile image
VersionV 1.0.0Latest, created on 
Jan 30, 2025 8:44 AM
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SHAP and Shapley Values Loop Nodes with a Custom Regression Model

This worflow shows how to use SHAP and Shapley Values Loop nodes and it creates Stacked Bar Charts similar to Force Plots for you to compare and understand the explanations. SHAP algorithm needs a smaller table to represent the validation set. This is achieved with the SHAP Summarizer Component.

This workflow showcases an example of using Gradient Boosted Trees Regression model, the workflow can be used with any other model as well.

External resources

  • SHAP Dependence Plot (documentation)
  • Verified Components
  • Explaining prediction models and individual predictions with feature contributions, Štrumbelj and Kononenko, 2014
  • SHAP (SHapley Additive exPlanations) - Python Library on GitHub
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Used extensions & nodes

Created with KNIME Analytics Platform version 5.4.0
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    KNIME Base nodesTrusted extension

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    KNIME H2O Machine Learning IntegrationTrusted extension

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    KNIME JavaScript ViewsTrusted extension

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    KNIME JavasnippetTrusted extension

    KNIME AG, Zurich, Switzerland

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    KNIME Machine Learning Interpretability ExtensionTrusted extension

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    KNIME Math Expression (JEP)Trusted extension

    KNIME AG, Zurich, Switzerland

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    KNIME Quick FormsTrusted extension

    KNIME AG, Zurich, Switzerland

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