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Spark H2O MOJO Predictor (Dimension Reduction)

KNIME Extension for MOJO nodes on Spark version 4.1.0.v201911110939 by KNIME AG, Zurich, Switzerland

This node applies a dimension reduction 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".

Dimension Reduction Settings

Dimension columns prefix
The prefix allows you to avoid duplicate column names. Can be empty.

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

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

Output Ports

Spark DataFrame/RDD containing the reduced input row.

Installation

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

KNIME 4.1
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