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Spark Decision Tree Learner

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

Max number of bins: Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.
Max depth: Maximum depth of the tree (>= 0).
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.
Class column: The classification column. Must be numeric.
Feature Columns: The feature columns to learn the model from. Supports only numeric columns.

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 from the below update site following our NodePit Product and Node Installation Guide:

v3.7

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

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 2.4.0.v201811301556

On NodePit since: 2018-08-10

Last update: 2024-12-26

KNIME versions: From v3.6 to v3.7

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