Spark Decision Tree Learner (MLlib)

This node applies the Apache Spark Decision / Regression Tree algorithm.

Please note that all data must be numeric, including the label column for classification tasks. Use the Spark Category To Number nodes to convert nominal values to numeric columns. The mapping dictionary of the converter node is the input of the second in port of this node.

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


Target column
The classification column. Must be numeric.
Feature Columns
The feature columns to learn the model from. Supports only numeric columns.
Max number of bins
Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.
Is classification
Indicates whether this is a classification or a regression task.
Quality measure
Criterion used for information gain calculation. Available methods: "gini" (recommended) or "entropy". For more details on the available methods see the MLlib documentation.
Max tree depth
Maximum depth of the tree (>= 0).

Input Ports

Input Spark DataFrame/RDD
PMML with the nominal values mapping dictionary

Output Ports

Spark MLlib Decision Tree Model


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