Spark H2O MOJO Predictor (Classification)

This node applies a classification MOJO (binomial, multinomial or ordinal) 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".

Classification Settings

Change prediction column name
Change the name of the prediction column.
Append individual class probabilities
Select to append the class probabilities of each class to the table. Useful for scoring models.
Suffix for probability columns
If class probabilities are appended, the suffix 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

Icon
The MOJO. Its model category must be either binomial, multinomial or ordinal.
Icon
Spark DataFrame/RDD for prediction. Missing values will be treated as NA .

Output Ports

Icon
Spark DataFrame/RDD containing the predicted class and, if selected, the individual class probabilities.

Views

This node has no views

Workflows

Links

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.