Spark Normalizer

This node normalizes the values of all selected (numeric) columns.

Options

Min-max normalization: Linear transformation of all values such that the minimum and maximum in each column are as given.
Z-score normalization (Gaussian): Linear transformation such that the values in each column are Gaussian-(0,1)-distributed, i.e. mean is 0.0 and standard deviation is 1.0.
Normalization by decimal scaling: The maximum value in a column (both positive and negative) is divided j-times by 10 until its absolute value is smaller or equal to 1. All values in the column are then divided by 10 to the power of j.

: Spark DataFrame/RDD with normalized columns.
: PMML document containing normalization parameters, which can be used in the "Spark Compiled Transformations Applier" node to normalize test data the same way as the training data has been normalized.

This node has no views

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

To use this node in KNIME, install the extension KNIME Extension for Apache Spark (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.0.v202406141453

On NodePit since: 2024-07-09

Last update: 2024-11-21

KNIME versions: Since v3.6

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.