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Changes  view on Meta::CPAN

      - Fixed test ./t/AI-Calibrate-NB.t so that test wouldn't fail.  Used to
        call is_deeply, which was failing on slight differences between
        floating point numbers.  Now compares with a small tolerance.

1.1   Thu Feb 28 19:00:06 2008
      - Added new function print_mapping
      - Added new test file AI-Calibrate-NB.t which, if AI::NaiveBayes1 is
        present, trains a classifier and calibrates it.

1.0   Thu Feb 05 11:37:31 2008
      - First public release to CPAN.

0.01  Thu Jan 24 11:37:31 2008
	- original version; created by h2xs 1.23 with options
		-XA -n AI::Calibrate

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

None known.  This implementation is straightforward but inefficient (its time
is O(n^2) in the length of the data series).  A linear time algorithm is
known, and in a later version of this module I'll probably implement it.

=head1 SEE ALSO

The AI::NaiveBayes1 perl module.

My paper "PAV and the ROC Convex Hull" has a good discussion of the PAV
algorithm, including examples:
L<http://home.comcast.net/~tom.fawcett/public_html/papers/PAV-ROCCH-dist.pdf>

If you want to read more about the general issue of classifier calibration,
here are some good papers, which are freely available on the web:

I<"Transforming classifier scores into accurate multiclass probability estimates">
by Bianca Zadrozny and Charles Elkan

I<"Predicting Good Probabilities With Supervised Learning">
by A. Niculescu-Mizil and R. Caruana