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

  # Find results for unseen instances
  my $result = $dtree->get_result
    (attributes => {outlook     => 'sunny',
                    temperature => 'hot',
                    humidity    => 'normal'});

=head1 DESCRIPTION

The C<AI::DecisionTree> module automatically creates so-called
"decision trees" to explain a set of training data.  A decision tree
is a kind of categorizer that use a flowchart-like process for
categorizing new instances.  For instance, a learned decision tree
might look like the following, which classifies for the concept "play
tennis":

                   OUTLOOK
                   /  |  \
                  /   |   \
                 /    |    \
           sunny/  overcast \rainy
               /      |      \

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

removed in future versions - especially with your help. =)

=over 4

=item No continuous attributes

In the current implementation, only discrete-valued attributes are
supported.  This means that an attribute like "temperature" can have
values like "cool", "medium", and "hot", but using actual temperatures
like 87 or 62.3 is not going to work.  This is because the values
would split the data too finely - the tree-building process would
probably think that it could make all its decisions based on the exact
temperature value alone, ignoring all other attributes, because each
temperature would have only been seen once in the training data.

The usual way to deal with this problem is for the tree-building
process to figure out how to place the continuous attribute values
into a set of bins (like "cool", "medium", and "hot") and then build
the tree based on these bin values.  Future versions of
C<AI::DecisionTree> may provide support for this.  For now, you have
to do it yourself.

=back

=head1 TO DO

All the stuff in the LIMITATIONS section.  Also, revisit the pruning