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  • 08_Regularized_Logistic_Regression
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Impact of Regularization in case of Logistic Regression

Regularization Gauss Laplace L1 L2
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The goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In the last step the results are summarized in an interactive javascript view.

External resources

  • Regularization for Logistic Regression: L1, L2, Gauss or Laplace?

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.3
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.3

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    KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.3

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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.3

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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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