0 ×

Dagging (3.6)

KNIME WEKA nodes version 2.10.2.v202003301209 by KNIME AG, Zurich, Switzerland

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 3.6)

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

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


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, Missing values, Nominal class, Binary 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: 1

Classifier Options

F: The number of folds for splitting the training set into smaller chunks for the base classifier. (default 10)

verbose: Whether to print some more information during building the classifier. (default is off)

S: Random number seed. (default 1)

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

W: Full name of base classifier. (default: weka.classifiers.functions.SMO)


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

no-checks: Turns off all checks - use with caution! Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0. (default: checks on)

C: The complexity constant C. (default 1)

N: Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

L: The tolerance parameter. (default 1.0e-3)

P: The epsilon for round-off error. (default 1.0e-12)

M: Fit logistic models to SVM outputs.

V: The number of folds for the internal cross-validation. (default -1, use training data)

W: The random number seed. (default 1)

K: The Kernel to use. (default: weka.classifiers.functions.supportVector.PolyKernel)


D: Enables debugging output (if available) to be printed. (default: off)

no-checks: Turns off all checks - use with caution! (default: checks on)

C: The size of the cache (a prime number), 0 for full cache and -1 to turn it off. (default: 250007)

E: The Exponent to use. (default: 1.0)

L: Use lower-order terms. (default: no)

Input Ports

Training data

Output Ports

Trained classifier


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.


To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:


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

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

Wait a sec! You want to explore and install nodes even faster? We highly recommend our NodePit for KNIME extension for your KNIME Analytics Platform. Browse NodePit from within KNIME, install nodes with just one click and share your workflows with NodePit Space.


You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.