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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

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