Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables
In: 8th European Conference on Machine Learning, 174-189, 1995.
(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.
S: Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)
X: Use cross validation to evaluate features. Use number of folds = 1 for leave one out CV. (Default = leave one out CV)
E: Performance evaluation measure to use for selecting attributes. (Default = accuracy for discrete class and rmse for numeric class)
I: Use nearest neighbour instead of global table majority.
R: Display decision table rules.
P: Specify a starting set of attributes. Eg. 1,3,5-7.
D: Direction of search. (default = 1).
N: Number of non-improving nodes to consider before terminating search.
S: Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1)
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, Nominal class, Binary class, Numeric class, Date class, Missing class values] Dependencies:  min # Instance: 1
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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