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Spark H2O MOJO Predictor (Regression)

KNIME Extension for MOJO nodes on Spark version 4.3.0.v202012011123 by KNIME AG, Zurich, Switzerland

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

Options

General Settings

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".

Regression Settings

Change prediction column name
Change the name of the prediction column.

Spark Settings

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

Input Ports

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

Output Ports

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Spark DataFrame/RDD containing the predicted value.

Best Friends (Incoming)

Installation

To use this node in KNIME, install KNIME Extension for MOJO nodes on Spark from the following update site:

KNIME 4.3

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

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Developers

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