Class for a Naive Bayes classifier using estimator classes
Numeric estimator precision values are chosen based on analysis of the training data.For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier.
The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.
For more information on Naive Bayes classifiers, see
George H.John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers.
In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 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.
K: Use kernel density estimator rather than normal distribution for numeric attributes
D: Use supervised discretization to process numeric attributes
O: Display model in old format (good when there are many classes)
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, Missing values, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 0
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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