This workflow shows how to use cross-validation in H2O using the KNIME H2O Nodes. In the example we use the H2O Random Forest to predict the multiclass response of the IRIS data set using 5-folds and evaluate the cross-validated performance.
EXAMPLES Server: 04_Analytics/15_H2O_Machine_Learning/04_H2O_Crossvalidation04_Analytics/15_H2O_Machine_Learning/04_H2O_Crossvalidation*
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* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually