0 ×

Winnow (3.6)

KNIME WEKA nodes version 2.10.2.v201808081048 by KNIME AG, Zurich, Switzerland

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. N. 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 3.6)

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

Classifier 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)

Input Ports

Training data

Output Ports

Trained classifier

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.

Update Site

To use this node in KNIME, install KNIME WEKA nodes from the following update site:

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.