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Spark Random Forests Learner

This node applies the Apache Spark Random Forests algorithm.

Please note that all data must be numeric, including the label column. 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

Number of trees

Number of trees in the random forest.

Max depth

Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

Max number of bins

Maximum number of bins used for splitting features.

Feature selection strategy

Number of features to consider for splits at each node. Supported:

auto
all
sqrt
log2
onethird

If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "sqrt".

Is classification

Select this option for binary or multiclass classification.

Quality measure

Criterion used for information gain calculation. Available methods:

gini (recommended)
entropy

For more details on the available methods see the MLlib documentation. Only available for classifications.

Seed

Random seed for bootstrapping and choosing feature subsets.

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 Random Forests 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: 2024-04-18

KNIME versions: From v3.6 to v3.7

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