H2O MOJO Predictor (Regression)

This node applies a regression 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".

Regression Settings

Change prediction column name

Change the name of the prediction column.

Views

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Workflows

• 0. Lubes_Sales_Pred-ModelNodePit Space
• 0. Lubes_Sales_Pred-ModelNodePit Space
• 07_Customer_prediction_with_H2OKNIME Hub
• 07_KNIMEでのH2Oによる顧客予測KNIME Hub
• 07_KNIMEでのH2Oによる顧客予測KNIME Hub
• Show all 17 workflows

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