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Python Transform

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Versionv1.0Latest, created on 
Oct 20, 2023 1:29 PM
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This Component transforms values of the user selected columns to normalized values by standardizing them across their mean values. The Component uses the Python Extension to perform the normalization with the Python Class “Standard Scaler preprocessing” in the Scikit learn library (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). This Component outputs the normalized values along with the Python Pickled Object that contains the parameters used for normalisation. DATA INPUT REQUIREMENTS - The input data should have numerical columns which can be transformed.

Component details

Input ports
  1. Type: Table
    Port 0
    Input Data.
Output ports
  1. Type: Table
    Port 0
    Transformed columns as per the user selection in the configuration panel.
  2. Type: Python
    Port 1
    Python Pickle Object which can be used to normalise the data, based on the normalisation parameters used in this Component.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.2 Note: Not all extensions may be displayed.
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    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.2

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    KNIME JavasnippetTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

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    KNIME Python Integration

    KNIME AG, Zurich, Switzerland

    Version 4.3.2

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    KNIME Quick FormsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.2

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This component does not have nodes, extensions, nested components and related workflows

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