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H2O MOJO Predictor (Autoencoder)

StreamableKNIME H2O Machine Learning Integration - MOJO Extension version 4.2.2.v202009241054 by KNIME AG

This node applies an autoencoder MOJO to an input dataset.

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 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".

Autoencoder Settings

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

Input Ports

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

Output Ports

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Table containing the predicted reconstruction.

Best Friends (Incoming)

Best Friends (Outgoing)

Installation

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

KNIME 4.2

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

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Developers

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