Spark Random Forest Learner

A random forest* is an ensemble of decision trees. Learning a random forest model means training a set of independent decision trees in parallel. This node uses the spark.ml random forest implementation to train a classification model in Spark. The target column must be nominal, whereas the feature columns can be either nominal or numerical.

Use the Spark Predictor (Classification) node to apply the learned model to unseen data.

Please refer to the Spark documentation for a full description of the underlying algorithm.

This node requires at least Apache Spark 2.0.

(*) RANDOM FORESTS is a registered trademark of Minitab, LLC and is used with Minitab’s permission.

Input ports

1. Type: Spark Data
   Input data
   Input Spark DataFrame with training data.

Output ports

1. Type: Table
   Feature importance measures
   Table with estimates of the importance of each feature. The features are listed in order of decreasing importance and are normalized to sum up to 1.
2. Type: Spark ML Model
   Spark ML Random Forest model (classification)
   Spark ML random forest model (classification)