KNIME WEKA nodes (3.7) version 4.2.0.v202007031307 by KNIME AG, Zurich, Switzerland
Class for building and using a Complement class Naive Bayes classifier. For more information see, Jason D
Rennie, Lawrence Shih, Jaime Teevan, David R.Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers.
In: ICML, 616-623, 2003.
P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.
(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.
N: Normalize the word weights for each class
S: Smoothing value to avoid zero WordGivenClass probabilities (default=1.0).
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, Missing values, Nominal class, Binary 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.
To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:
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