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sub _delete_value {
  my ($self, $instance, $attr) = @_;
  my $val = $self->_value($instance, $attr);
  return unless defined $val;
  
  $instance->set_value($self->{attributes}{$attr}, 0);
  return $val;
}

sub _value {
  my ($self, $instance, $attr) = @_;
  return unless exists $self->{attributes}{$attr};
  my $val_int = $instance->value_int($self->{attributes}{$attr});
  return $self->{attribute_values_reverse}{$attr}[$val_int];
}



1;
__END__

=head1 NAME

AI::DecisionTree - Automatically Learns Decision Trees

=head1 VERSION

version 0.11

=head1 SYNOPSIS

  use AI::DecisionTree;
  my $dtree = new AI::DecisionTree;
  
  # A set of training data for deciding whether to play tennis
  $dtree->add_instance
    (attributes => {outlook     => 'sunny',
                    temperature => 'hot',
                    humidity    => 'high'},
     result => 'no');
  
  $dtree->add_instance
    (attributes => {outlook     => 'overcast',
                    temperature => 'hot',
                    humidity    => 'normal'},
     result => 'yes');

  ... repeat for several more instances, then:
  $dtree->train;
  
  # 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
               /      |      \
          HUMIDITY    |       WIND
          /  \       *no*     /  \
         /    \              /    \
    high/      \normal      /      \
       /        \    strong/        \weak
     *no*      *yes*      /          \
                        *no*        *yes*

(This example, and the inspiration for the C<AI::DecisionTree> module,
come directly from Tom Mitchell's excellent book "Machine Learning",
available from McGraw Hill.)

A decision tree like this one can be learned from training data, and
then applied to previously unseen data to obtain results that are
consistent with the training data.

The usual goal of a decision tree is to somehow encapsulate the
training data in the smallest possible tree.  This is motivated by an
"Occam's Razor" philosophy, in which the simplest possible explanation
for a set of phenomena should be preferred over other explanations.
Also, small trees will make decisions faster than large trees, and
they are much easier for a human to look at and understand.  One of
the biggest reasons for using a decision tree instead of many other
machine learning techniques is that a decision tree is a much more
scrutable decision maker than, say, a neural network.

The current implementation of this module uses an extremely simple
method for creating the decision tree based on the training instances.
It uses an Information Gain metric (based on expected reduction in
entropy) to select the "most informative" attribute at each node in
the tree.  This is essentially the ID3 algorithm, developed by
J. R. Quinlan in 1986.  The idea is that the attribute with the
highest Information Gain will (probably) be the best attribute to
split the tree on at each point if we're interested in making small
trees.

=head1 METHODS

=head2 Building and Querying the Tree

=over 4

=item new(...parameters...)

Creates a new decision tree object and returns it.  Accepts the
following parameters:

=over 4

=item noise_mode

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

     'overcast' => 'yes',
 } ]

This is slightly remniscent of how XML::Parser returns the parsed 
XML tree.

Note that while the ordering in the hashes is unpredictable, the 
nesting is in the order in which the criteria will be checked at 
decision-making time.

=item rule_statements()

Returns a list of strings that describe the tree in rule-form.  For
instance, for the tree diagram above, the following list would be
returned (though not necessarily in this order - the order is
unpredictable):

  if outlook='rain' and wind='strong' -> 'no'
  if outlook='rain' and wind='weak' -> 'yes'
  if outlook='sunny' and humidity='normal' -> 'yes'
  if outlook='sunny' and humidity='high' -> 'no'
  if outlook='overcast' -> 'yes'

This can be helpful for scrutinizing the structure of a tree.

Note that while the order of the rules is unpredictable, the order of
criteria within each rule reflects the order in which the criteria
will be checked at decision-making time.

=item as_graphviz()

Returns a C<GraphViz> object representing the tree.  Requires that the
GraphViz module is already installed, of course.  The object returned
will allow you to create PNGs, GIFs, image maps, or whatever graphical
representation of your tree you might want.  

A C<leaf_colors> argument can specify a fill color for each leaf node
in the tree.  The keys of the hash should be the same as the strings
appearing as the C<result> parameters given to C<add_instance()>, and
the values should be any GraphViz-style color specification.

Any additional arguments given to C<as_graphviz()> will be passed on
to GraphViz's C<new()> method.  See the L<GraphViz> docs for more
info.

=back

=head1 LIMITATIONS

A few limitations exist in the current version.  All of them could be
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
algorithm to see how it can be improved.

=head1 AUTHOR

Ken Williams, ken@mathforum.org

=head1 SEE ALSO

Mitchell, Tom (1997).  Machine Learning.  McGraw-Hill. pp 52-80.

Quinlan, J. R. (1986).  Induction of decision trees.  Machine
Learning, 1(1), pp 81-106.

L<perl>, L<GraphViz>

=cut