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  • 02_SHAP_and_Shapley_Values
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SHAP and Shapley Values Loop Nodes with a Custom Regression Model

Machine learning interpretability Mli Force plot Shapley Shap
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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

Used extensions & nodes

Created with KNIME Analytics Platform version 4.6.3
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.6.1, 4.6.2

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    Version 4.6.2

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    KNIME Javasnippet Trusted extension

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    Version 4.6.0

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    KNIME Machine Learning Interpretability Extension Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

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    KNIME Quick Forms Trusted extension

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