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.

Options

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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Installation

To use this node in KNIME, install the extension KNIME Extension for Apache Spark (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.4

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.4.0.v202411151159

On NodePit since: 2024-12-06

Last update: 2024-12-17

KNIME versions: Since v4.0

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