StackingC (3.6) (legacy)

Implements StackingC (more efficient version of stacking). For more information, see A.K. Seewald: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. In: Nineteenth International Conference on Machine Learning, 554-561, 2002. Note: requires meta classifier to be a numeric prediction scheme.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Options

Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, Nominal class, Binary class, Numeric class, Date class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational class, Missing class values, Only multi-Instance data] min # Instance: 10

Classifier Options

M: Full name of meta classifier, followed by options. Must be a numeric prediction scheme. Default: Linear Regression.

X: Sets the number of cross-validation folds.

S: Random number seed. (default 1)

B: Full class name of classifier to include, followed by scheme options. May be specified multiple times. (default: "weka.classifiers.rules.ZeroR")

D: If set, classifier is run in debug mode and may output additional info to the console

Input Ports

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Training data

Output Ports

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Trained classifier

Popular Predecessors

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Popular Successors

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Views

Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Workflows

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Links

Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

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

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 2.10.2.v202312081535
On NodePit since: 2024-07-09
Last update: 2024-11-21
KNIME versions: Since v5.2

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