Class for using linear regression for prediction
Uses the Akaike criterion for model selection, and is able to deal with weighted instances.
(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.
D: Produce debugging output. (default no debugging output)
S: Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
C: Do not try to eliminate colinear attributes.
R: Set ridge parameter (default 1.0e-8).
minimal: Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)
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, 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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