| Name : perl-AI-Calibrate
| |
| Version : 1.5
| Vendor : obs://build_opensuse_org/devel:languages:perl
|
| Release : lp154.7.1
| Date : 2023-01-27 16:04:48
|
| Group : Development/Libraries/Perl
| Source RPM : perl-AI-Calibrate-1.5-lp154.7.1.src.rpm
|
| Size : 0.02 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Perl module for producing probabilities from classifier scores
|
Description :
Classifiers usually return some sort of an instance score with their
classifications. These scores can be used as probabilities in various
calculations, but first they need to be _calibrated_. Naive Bayes, for
example, is a very useful classifier, but the scores it produces are
usually \"bunched\" around 0 and 1, making these scores poor probability
estimates. Support vector machines have a similar problem. Both classifier
types should be calibrated before their scores are used as probability
estimates.
This module calibrates classifier scores using a method called the Pool
Adjacent Violators (PAV) algorithm. After you train a classifier, you take
a (usually separate) set of test instances and run them through the
classifier, collecting the scores assigned to each. You then supply this
set of instances to the calibrate function defined here, and it will return
a set of ranges mapping from a score range to a probability estimate.
For example, assume you have the following set of instance results from
your classifier. Each result is of the form \'[ASSIGNED_SCORE, TRUE_CLASS]\':
my $points = [
[.9, 1],
[.8, 1],
[.7, 0],
[.6, 1],
[.55, 1],
[.5, 1],
[.45, 0],
[.4, 1],
[.35, 1],
[.3, 0 ],
[.27, 1],
[.2, 0 ],
[.18, 0],
[.1, 1 ],
[.02, 0]
];
If you then call calibrate($points), it will return this structure:
[
[.9, 1 ],
[.7, 3/4 ],
[.45, 2/3 ],
[.3, 1/2 ],
[.2, 1/3 ],
[.02, 0 ]
]
This means that, given a SCORE produced by the classifier, you can map the
SCORE onto a probability like this:
SCORE >= .9 prob = 1
.9 > SCORE >= .7 prob = 3/4
.7 > SCORE >= .45 prob = 2/3
.45 > SCORE >= .3 prob = 3/4
.2 > SCORE >= .7 prob = 3/4
.02 > SCORE prob = 0
For a realistic example of classifier calibration, see the test file
t/AI-Calibrate-NB.t, which uses the AI::NaiveBayes1 module to train a Naive
Bayes classifier then calibrates it using this module.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/perl:/CPAN-A/15.4/noarch |
