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.


Dagging 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)

Select target 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

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Popular Successors

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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.


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