Dagging (3.7)


This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier

Predictions are made via averaging, since all the generated base classifiers are put into the Vote meta classifier.

Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data.

For more information, see:

Ting, K.M., Witten, I.

H.: Stacking Bagged and Dagged Models.In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

(based on WEKA 3.7)

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

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

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Classifier Trained model

Find here

Analytics > Mining > Weka > Weka (3.7) > Classification Algorithms > meta

Make sure to have this extension installed:

KNIME Weka Data Mining Integration (3.7)

Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site

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