Grokbase Groups Perl ai July 2001
FAQ
Hi,

I hope to get one more release out the door before TPC, with an improved
threshold-setting procedure in kNN. And it would be nice to figure out
why NaiveBayes results are so damn terrible.

The uploaded file

AI-Categorize-0.04.tar.gz

has entered CPAN as

file: $CPAN/authors/id/K/KW/KWILLIAMS/AI-Categorize-0.04.tar.gz
size: 20334 bytes
md5: d6c2496f78144760415398f848db9772

Changes since 0.03:

- Reworked the AI::Categorize::Evaluate module so that it much better
addresses the issue of how to specify both general info for all tests
and specific info for each test. This makes it possible to test
the results of using different initialization parameters, for
instance, or the results on varying test sets.

- Made some changes to the way AI::Categorize::Evaluate stores its results
between stages of the testing. This isn't stable yet.

- Added a testing summary at the end of
AI::Categorize::Evaluate->evaluate_test_set.

- Created the 'drmath-1.00' corpus, which I'll use as a stable corpus for
benchmarking the differences various changes to the code has. It's large,
so I'm not distributing it with the modules. Write me if you want it.

- The kNN and NaiveBayes classifiers now trim their list of corpus features
(words) to get rid of seldom-used features. This can improve speed
and quality. Preliminary results (using F1 as a quality measure) are:
corpus is drmath-1.00 with 12379 unique features.
kNN using 100% of features: F1=0.180, testing time=1384 sec
kNN using 20% of features: F1=0.178, testing time=1060 sec
kNN using 10% of features: F1=0.180 testing time=1050 sec
NB using 100% of features: F1=0.037, testing time= 102 sec
NB using 20% of features: F1=0.041, testing time= 72 sec
NB using 10% of features: F1=0.039, testing time= 93 sec
See the 'features_kept' item in the kNN and NaiveBayes docs.

- Created the new AI::Categorize::VectorBased class, which kNN now inherits
from, and which can be a base class for other classifiers (like SVM, hint
hint).

- Started to clean up print() statements throughout the code. They
give feedback on training progress, but sometimes you probably don't
want to see it.

- Moved the example script 'evaluate.pl' to the new 'eg/' directory,
because otherwise 'make install' would install it into site_perl/ .
If you installed previous versions of AI::Categorize, you may want
to remove 'evaluate.pl' from your site_perl/ directory.


------------------- -------------------
Ken Williams Last Bastion of Euclidity
ken@forum.swarthmore.edu The Math Forum

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