NAME
Algorithm::NaiveBayes - Bayesian prediction of categories
SYNOPSIS
use Algorithm::NaiveBayes;
my $nb = Algorithm::NaiveBayes->new;
$nb->add_instance
(attributes => {foo => 1, bar => 1, baz => 3},
label => 'sports');
$nb->add_instance
(attributes => {foo => 2, blurp => 1},
label => ['sports', 'finance']);
... repeat for several more instances, then:
$nb->train;
# Find results for unseen instances
my $result = $nb->predict
(attributes => {bar => 3, blurp => 2});
DESCRIPTION
This module implements the classic "Naive Bayes" machine learning algorithm.
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.
A paper by Fabrizio Sebastiani provides a really good introduction to text
categorization:
http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf
METHODS
new()
Creates a new "Algorithm::NaiveBayes" object and returns it. The
following parameters are accepted:
purge
If set to a true value, the "do_purge()" method will be invoked
during "train()". The default is true. Set this to a false value if
you'd like to be able to add additional instances after training and
then call "train()" again.
add_instance( attributes => HASH, label => STRING|ARRAY )
Adds a training instance to the categorizer. The "attributes" parameter
contains a hash reference whose keys are string attributes and whose
values are the weights of those attributes. For instance, if you're
categorizing text documents, the attributes might be the words of the
document, and the weights might be the number of times each word occurs
in the document.
The "label" parameter can contain a single string or an array of
strings, with each string representing a label for this instance. The
labels can be any arbitrary strings. To indicate that a document has no
applicable labels, pass an empty array reference.
train()
Calculates the probabilities that will be necessary for categorization
using the "predict()" method.
predict( attributes => HASH )
Use this method to predict the label of an unknown instance. The
attributes should be of the same format as you passed to
"add_instance()". "predict()" returns a hash reference whose keys are
the names of labels, and whose values are the score for each label.
Scores are between 0 and 1, where 0 means the label doesn't seem to
apply to this instance, and 1 means it does.
In practice, scores using Naive Bayes tend to be very close to 0 or 1
because of the way normalization is performed. I might try to alleviate
this in future versions of the code.
labels()
Returns a list of all the labels the object knows about (in no
particular order), or the number of labels if called in a scalar
context.
do_purge()
Purges training instances and their associated information from the
NaiveBayes object. This can save memory after training.
purge()
Returns true or false depending on the value of the object's "purge"
property. An optional boolean argument sets the property.
THEORY
Bayes' Theorem is a way of inverting a conditional probability. It states:
P(y|x) P(x)
P(x|y) = -------------
P(y)
The notation "P(x|y)" means "the probability of "x" given "y"." See also the
section on "http://mathforum.org/dr.math/problems/battisfore.03.22.99.html"
for a simple but complete example of Bayes' Theorem.
In this case, we want to know the probability of a given category given a
certain string of words in a document, so we have:
P(words | cat) P(cat)
P(cat | words) = --------------------
P(words)
We have applied Bayes' Theorem because "P(cat | words)" is a difficult
quantity to compute directly, but "P(words | cat)" and "P(cat)" are
accessible (see below).
The greater the expression above, the greater the probability that the given
document belongs to the given category. So we want to find the maximum
value. We write this as
P(words | cat) P(cat)
Best category = ArgMax -----------------------
cat in cats P(words)
Since "P(words)" doesn't change over the range of categories, we can get rid
of it. That's good, because we didn't want to have to compute these values
anyway. So our new formula is:
Best category = ArgMax P(words | cat) P(cat)
cat in cats
Finally, we note that if "w1, w2, ... wn" are the words in the document,
then this expression is equivalent to:
Best category = ArgMax P(w1|cat)*P(w2|cat)*...*P(wn|cat)*P(cat)
cat in cats
That's the formula I use in my document categorization code. The last step
is the only non-rigorous one in the derivation, and this is the "naive" part
of the Naive Bayes technique. It assumes that the probability of each word
appearing in a document is unaffected by the presence or absence of each
other word in the document. We assume this even though we know this isn't
true: for example, the word "iodized" is far more likely to appear in a
document that contains the word "salt" than it is to appear in a document
that contains the word "subroutine". Luckily, as it turns out, making this
assumption even when it isn't true may have little effect on our results, as
the following paper by Pedro Domingos argues: the section on
"http://www.cs.washington.edu/homes/pedrod/mlj97.ps.gz"
HISTORY
My first implementation of a Naive Bayes algorithm was in the now-obsolete
AI::Categorize module, first released in May 2001. I replaced it with the
Naive Bayes implementation in AI::Categorizer (note the extra 'r'), first
released in July 2002. I then extracted that implementation into its own
module that could be used outside the framework, and that's what you see
here.
AUTHOR
Ken Williams, ken@mathforum.org
SEE ALSO
AI::Categorizer(3), the perl manpage.