Winnow (3.7)

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N

Littlestone (1988).Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm.

Machine Learning.2:285-318.


Littlestone (1989).Mistake bounds and logarithmic linear-threshold learning algorithms.

University of California, Santa Cruz.

Does classification for problems with nominal attributes (which it converts into binary attributes).

(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.


Winnow Options

L: Use the baLanced version (default false)

I: The number of iterations to be performed. (default 1)

A: Promotion coefficient alpha. (default 2.0)

B: Demotion coefficient beta. (default 0.5)

H: Prediction threshold. (default -1.0 == number of attributes)

W: Starting weights. (default 2.0)

S: Default random seed. (default 1)

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, Missing values, Binary class, Missing class values] Dependencies: [] min # Instance: 0

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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