Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection. .
(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.
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
S: Set sample size (default: 4)
G: Set the seed used to generate samples (default: 0)
D: Produce debugging output (default no debugging output)
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A zipped version of the software site can be downloaded here.
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