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

=head1 FUNCTIONS

=over 4

=item B<calibrate>

This is the main calibration function.  The calling form is:

my $calibrated = calibrate( $data, $sorted);

$data looks like: C<[ [score, class], [score, class], [score, class]...]>
Each score is a number.  Each class is either 0 (negative class) or 1
(positive class).

$sorted is boolean (0 by default) indicating whether the data are already
sorted by score.  Unless this is set to 1, calibrate() will sort the data
itself.

Calibrate returns a reference to an ordered list of references:

  [ [score, prob], [score, prob], [score, prob] ... ]

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

        printf("%0.3f > SCORE >= %0.3f     prob = %0.3f\n",
               $last_bound, 0, 0);
    }
}

=head1 DETAILS

The PAV algorithm is conceptually straightforward.  Given a set of training
cases ordered by the scores assigned by the classifier, it first assigns a
probability of one to each positive instance and a probability of zero to each
negative instance, and puts each instance in its own group.  It then looks, at
each iteration, for adjacent violators: adjacent groups whose probabilities
locally increase rather than decrease.  When it finds such groups, it pools
them and replaces their probability estimates with the average of the group's
values.  It continues this process of averaging and replacement until the
entire sequence is monotonically decreasing.  The result is a sequence of
instances, each of which has a score and an associated probability estimate,
which can then be used to map scores into probability estimates.

For further information on the PAV algorithm, you can read the section in my
paper referenced below.