Spark H2O MOJO Predictor (Cluster Assigner)

This node applies a clustering MOJO to an incoming Spark DataFrame/RDD.

Options

Enforce presence of all feature columns: If checked, the node will fail if any of the feature columns used for learning the MOJO is missing. Otherwise, a warning will be displayed and the missing columns are treated as NA by the MOJO predictor.
Fail if a prediction exception occurs: If checked, the node will fail if the prediction of a row fails. Otherwise, a missing value will be the output.
Treat unknown categorical values as missing values: By default, H2O does not handle the case that a categorical feature column contains a value that was not present during model training. If this option is enabled, H2O will convert these values to NA, i.e. treat them as missing values. If this option is disabled, the node will either fail or missing values will be in the output depending on the setting "Fail if a prediction exception occurs".

Upload MOJO dependency: If checked, the MOJO dependency (genmodel jar file) will be uploaded to the cluster. Otherwise depend on cluster side provided dependency.

: The MOJO. Its model category must be clustering.
: Spark DataFrame/RDD for prediction. Missing values will be treated as NA .

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To use this node in KNIME, install the extension KNIME Extension for MOJO nodes on Spark 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.v202406181441

On NodePit since: 2024-07-09

Last update: 2024-11-21

KNIME versions: Since v3.6

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