A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
(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: [Numeric attributes, Date attributes, Missing values, Numeric class, Date class, Missing class values] Dependencies:  min # Instance: 1
filter: The PLS filter to use. Full classname of filter to include, followed by scheme options. (default: weka.filters.supervised.attribute.PLSFilter)
D: If set, classifier is run in debug mode and may output additional info to the console
D: Turns on output of debugging information.
C: The number of components to compute. (default: 20)
U: Updates the class attribute as well. (default: off)
M: Turns replacing of missing values on. (default: off)
A: The algorithm to use. (default: PLS1)
P: The type of preprocessing that is applied to the data. (default: center)
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) 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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