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lib/Algorithm/DecisionTree.pm  view on Meta::CPAN

methods listed above for classifying individual data samples with boosting and for
bulk classification when you place all your test samples in a single file.


=head1 USING RANDOMIZED DECISION TREES

Consider the following two situations that call for using randomized decision trees,
meaning multiple decision trees that are trained using data extracted randomly from a
large database of training samples: 

(1) Consider a two-class problem for which the training database is grossly
imbalanced in how many majority-class samples it contains vis-a-vis the number of
minority class samples.  Let's assume for a moment that the ratio of majority class
samples to minority class samples is 1000 to 1.  Let's also assume that you have a
test dataset that is drawn randomly from the same population mixture from which the
training database was created.  Now consider a stupid data classification program
that classifies everything as belonging to the majority class.  If you measure the
classification accuracy rate as the ratio of the number of samples correctly
classified to the total number of test samples selected randomly from the population,
this classifier would work with an accuracy of 99.99%.