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  • RF Feature Importance (based on Python Library)
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RF Feature Importance (based on Python Library)

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This Component can be used to visualise the Feature/variable importance in a binary classification sceanrio. It used the Random forest importance calculater function in python to calculate the importance of the variables. The python script nodes are encapsulated in this component, the user do not have to set up the python environment, the conda propagation node will set up all the required libararies to the user system Can be used on KAP version 4.3 and above

Component details

Input ports
  1. Type: Table
    Input
    Input data with binary target
Output ports
  1. Type: Table
    Output
    Feature Importance table

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime
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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

    knime
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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

    knime
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Legal

By using or downloading the component, you agree to our terms and conditions.

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