Algorithm-Classifier-NaiveBayes

A multinomial naive Bayes text classifier for Perl.

Features...

• ngrams :: Why limit yourself to a single token when you can also optionally learn near by tokens as well!

• Classes are not predefined. Training a new class name creates it and if untraining completely removes everything the class is removed as well.

• Configurable tokenization. Token splitting regex, lowercasing, and an optional stop word regex.

• Models can be untrained as well as trained, so mistakes can be corrected without retraining from scratch.

• Models can be saved to and loaded from JSON, either as a string or a file. File writes are atomic.

• smoothing :: Choose between Laplace(+1) or Lidstone(+alpha).

• token_weighting :: A choice between the traditional count and binary where it is only coulded once per doc for training/classifying.

• priors :: A choice between the traditional trained and uniform, which gives every class a equal prior(useful for when training is unbalanced.

Usage

use Algorithm::Classifier::NaiveBayes;

my $nb = Algorithm::Classifier::NaiveBayes->new;

# train it with examples of each class
$nb->train( 'spam', 'buy cheap pills now' );
$nb->train( 'spam', 'cheap watches for sale' );
$nb->train( 'ham',  'meeting at noon tomorrow' );
$nb->train( 'ham',  'lunch with the team' );

# classify some new text
my $class = $nb->classify('cheap pills for sale');
# $class is now 'spam'

# or get the score for every class as well
my ( $best, $scores ) = $nb->classify('cheap pills for sale');

# save the model for later and load it again
$nb->save('model.json');

my $loaded = Algorithm::Classifier::NaiveBayes->new;
$loaded->load('model.json');

For full documentation see the POD for the module. Runnable examples, including small command line training and classification scripts, can be found under examples/.

A command line tool, nb_tool, is also included for working with saved models without writing any code.

nb_tool train -m model.json -c spam buy cheap pills now
nb_tool train -m model.json -c ham meeting at noon tomorrow

nb_tool classify -m model.json -p cheap pills
nb_tool explain -m model.json you have won a free cruise

nb_tool info -m model.json
nb_tool tokens -m model.json spam
nb_tool prune -m model.json 2
nb_tool tweak -m model.json --smoothing lidstone --alpha 0.1
nb_tool untrain -m model.json -c spam buy cheap pills now

See nb_tool commands and nb_tool help <command> for the details.

Installation

The non-core modules below are required.

• File::Slurp

FreeBSD

pkg install perl5 p5-File-Slurp p5-App-cpanminus
cpanm Algorithm::Classifier::NaiveBayes

Debian

apt-get install perl libfile-slurp-perl cpanminus
cpanm Algorithm::Classifier::NaiveBayes

Source

To install this module from this repo, run the following commands.

perl Makefile.PL
make
make test
make install

Support and Documentation

After installing, you can find documentation for this module with the perldoc command.

perldoc Algorithm::Classifier::NaiveBayes

You can also look for information at:

• RT, CPAN's request tracker (report bugs here) https://rt.cpan.org/NoAuth/Bugs.html?Dist=Algorithm-Classifier-NaiveBayes

• Search CPAN https://metacpan.org/release/Algorithm-Classifier-NaiveBayes

License and Copyright

This software is Copyright (c) 2026 by Zane C. Bowers-Hadley.

This is free software, licensed under:

The GNU Lesser General Public License, Version 2.1, February 1999