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H2O MOJO Predictor (Dimension Reduction)

StreamableKNIME H2O Machine Learning Integration - MOJO Extension version 4.4.0.v202105060938 by KNIME AG

This node applies a dimension reduction MOJO to an input dataset.


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 and a warning will be given.
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.

Input Ports

The MOJO. Its model category must be dimension reduction.
Table for prediction. Missing values will be treated as NA .

Output Ports

Table containing the reduced input row.

Best Friends (Incoming)

Best Friends (Outgoing)


To use this node in KNIME, install KNIME H2O Machine Learning Integration - MOJO Extension from the following update site:


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

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