KNIME WEKA nodes (3.7) version 4.1.0.v201911281246 by KNIME AG, Zurich, Switzerland
Implementation of the voted perceptron algorithm by Freund and Schapire
Globally replaces all missing values, and transforms nominal attributes into binary ones.
For more information, see:
E.Schapire: Large margin classification using the perceptron algorithm.
In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
(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.
I: The number of iterations to be performed. (default 1)
E: The exponent for the polynomial kernel. (default 1)
S: The seed for the random number generation. (default 1)
M: The maximum number of alterations allowed. (default 10000)
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, Date attributes, Missing values, Binary class, Missing class values] Dependencies:  min # Instance: 0
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:
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