Package

weka.classifiers.bayes

Synopsis

Multinomial naive bayes for text data. Operates directly (and only) on String attributes. Other types of input attributes are accepted but ignored during training and classification

Options

The table below describes the options available for NaiveBayesMultinomialText.

Option	Description
LNorm	The LNorm to use for document length normalization.
debug	If set to true, classifier may output additional info to the console.
lowercaseTokens	Whether to convert all tokens to lowercase
minWordFrequency	Ignore any words that don't occur at least min frequency times in the training data. If periodic pruning is turned on, then the dictionary is pruned according to this value
norm	The norm of the instances after normalization.
normalizeDocLength	If true then document length is normalized according to the settings for norm and lnorm
periodicPruning	How often (number of instances) to prune the dictionary of low frequency terms. 0 means don't prune. Setting a positive integer n means prune after every n instances
stemmer	The stemming algorithm to use on the words.
stopwords	The file containing the stopwords (if this is a directory then the default ones are used).
tokenizer	The tokenizing algorithm to use on the strings.
useStopList	If true, ignores all words that are on the stoplist.
useWordFrequencies	Use word frequencies rather than binary bag of words representation

Capabilities

The table below describes the capabilities of NaiveBayesMultinomialText.

Capability	Supported
Class	Nominal class, Binary class, Missing class values
Attributes	Numeric attributes, Missing values, Date attributes, Unary attributes, Empty nominal attributes, Binary attributes, Nominal attributes, String attributes
Min # of instances	0