AI-NaiveBayes

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README.pod  view on Meta::CPAN

    my $best_category = $result->best_category;
    
=head1 DESCRIPTION

This module implements the classic "Naive Bayes" machine learning
algorithm.  This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.  

Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start 
you can use the limited C<train> class method that trains the 
classifier in a default way.

The classifier object is immutable.

It is a well-studied probabilistic algorithm often used in
automatic text categorization.  Compared to other algorithms (kNN,
SVM, Decision Trees), it's pretty fast and reasonably competitive in
the quality of its results.

README.pod  view on Meta::CPAN

text categorization:
L<http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf>

=head1 METHODS

=over 4

=item new( model => $model )

Internal. See L<AI::NaiveBayes::Learner> to learn how to create a C<AI::NaiveBayes>
classifier from training data.

=item train( LIST of HASHREFS )

Shortcut for creating a trained classifier using L<AI::NaiveBayes::Learner> default
settings. 
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.

=item classify( HASHREF )

lib/AI/NaiveBayes.pm  view on Meta::CPAN

    my $best_category = $result->best_category;

=head1 DESCRIPTION

This module implements the classic "Naive Bayes" machine learning
algorithm.  This is a low level class that accepts only pre-computed feature-vectors
as input, see L<AI::Classifier::Text> for a text classifier that uses
this class.  

Creation of C<AI::NaiveBayes> classifier object out of training
data is done by L<AI::NaiveBayes::Learner>. For quick start 
you can use the limited C<train> class method that trains the 
classifier in a default way.

The classifier object is immutable.

It is a well-studied probabilistic algorithm often used in
automatic text categorization.  Compared to other algorithms (kNN,
SVM, Decision Trees), it's pretty fast and reasonably competitive in
the quality of its results.

lib/AI/NaiveBayes.pm  view on Meta::CPAN

text categorization:
L<http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf>

=head1 METHODS

=over 4

=item new( model => $model )

Internal. See L<AI::NaiveBayes::Learner> to learn how to create a C<AI::NaiveBayes>
classifier from training data.

=item train( LIST of HASHREFS )

Shortcut for creating a trained classifier using L<AI::NaiveBayes::Learner> default
settings. 
Arguments are passed to the C<add_example> method of the L<AI::NaiveBayes::Learner>
object one by one.

=item classify( HASHREF )

lib/AI/NaiveBayes/Learner.pm  view on Meta::CPAN

        attributes => { sheep => 1, very => 1, valuable => 1, farming => 1 },
        labels => ['farming'] 
    );

    my $classifier = $learner->classifier;

=head1 DESCRIPTION

This is a trainer of AI::NaiveBayes classifiers.  It saves information passed
by the C<add_example> method from
training data into internal structures and then constructs a classifier when
the C<classifier> method is called.

=head1 ATTRIBUTES

=over 4

=item C<features_kept>

Indicates how many features should remain after calculating probabilities. By
default all of them will be kept. For C<features_kept> > 1, C<features_kept> of

lib/AI/NaiveBayes/Learner.pm  view on Meta::CPAN

C<AI::NaiveBayes>

=back

=head1 METHODS

=over 4

=item C<add_example( attributes => HASHREF, labels => LIST )>

Saves the information from a training example into internal data structures.
C<attributes> should be of the form of 
    { feature1 => weight1, feature2 => weight2, ... }
C<labels> should be a list of strings denoting one or more classes to which the example belongs.

=item C<classifier()>

    Creates an AI::NaiveBayes classifier based on the data accumulated before.

=back

=head1 UTILITY SUBS

=over 4

=item C<add_hash>

=back