Algorithm-DecisionTree

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          FOR USING A DECISION TREE CLASSIFIER INTERACTIVELY


Starting with Version 1.6 of the module, you can use the DecisionTree
classifier in an interactive mode.  In this mode, after you have
constructed the decision tree, the user is prompted for answers to the
questions regarding the feature tests at the nodes of the tree.  Depending
on the answer supplied by the user at a node, the classifier takes a path
corresponding to the answer to descend down the tree to the next node, and
so on.  To get a feel for using a decision tree in this mode, examine the
script

        classify_by_asking_questions.pl

Execute the script as it is and see what happens.

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

that you declared at top of your training file or that the values you have for your
features are legal vis-a-vis the earlier declarations of the values in the training
file.  Another safety feature incorporated in this version is the non-consideration
of classes that are declared at the top of the training file but that have no sample
records in the file.

B<Version 1.6> uses probability caching much more extensively compared to the previous
versions.  This should result in faster construction of large decision trees.
Another new feature in Version 1.6 is the use of a decision tree for interactive
classification. In this mode, after you have constructed a decision tree from the
training data, the user is prompted for answers to the questions pertaining to the
feature tests at the nodes of the tree.

Some key elements of the documentation were cleaned up and made more readable in
B<Version 1.41>.  The implementation code remains unchanged from Version 1.4.

B<Version 1.4> should make things faster (and easier) for folks who want to use this
module with training data that creates very large decision trees (that is, trees with
tens of thousands or more decision nodes).  The speedup in Version 1.4 has been
achieved by eliminating duplicate calculation of probabilities as the tree grows.  In
addition, this version provides an additional constructor parameter,

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

probabilities.  The hash that is returned by the above call also includes a special
key-value pair for a key named C<solution_path>.  The value associated with this key
is an anonymous array that holds the path, in the form of a list of nodes, from the
root node to the leaf node in the decision tree where the final classification was
made.


=item B<classify_by_asking_questions($root_node):>

This method allows you to use a decision-tree based classifier in an interactive
mode.  In this mode, a user is prompted for answers to the questions pertaining to
the feature tests at the nodes of the tree.  The syntax for invoking this method is:

    my $classification = $dt->classify_by_asking_questions($root_node);

where C<$dt> is an instance of the C<Algorithm::DecisionTree> class returned by a
call to C<new()> and C<$root_node> the root node of the decision tree returned by a
call to C<construct_decision_tree_classifier()>.

=back

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

string C<""> there for each data record. This is demonstrated by the following call:

    classify_test_data_in_a_file.pl   training4.csv   test4_no_class_labels.csv   out4.csv

The following script in the C<Examples> directory

    classify_by_asking_questions.pl

shows how you can use a decision-tree classifier interactively.  In this mode, you
first construct the decision tree from the training data and then the user is
prompted for answers to the feature tests at the nodes of the tree.

If your training data has a feature whose values span a large range and, at the same
time, are characterized by a heavy-tail distribution, you should look at the script

    construct_dt_for_heavytailed.pl                                                     

to see how to use the option C<number_of_histogram_bins> in the call to the
constructor.  This option was introduced in Version 2.22 for dealing with such
features.  If you do not set this option, the module will use the default value of
500 for the number of points at which to sample the value range for such a feature.

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

I thank Slaven Rezic for pointing out that the module worked with Perl 5.14.x.  For
Version 2.22, I had set the required version of Perl to 5.18.0 since that's what I
used for testing the module. Slaven's feedback in the form of the Bug report
C<#96547> resulted in Version 2.23 of the module.  Version 2.25 further downshifts
the required version of Perl to 5.10.

On the basis of the report posted by Slaven at C<rt.cpan.org> regarding Version 2.27,
I am removing the Perl version restriction altogether from Version 2.30.  Thanks
Slaven!

Version 3.43 was prompted by a bug report sent by Jan Trukenmüller regarding a
missing backslash in a regex in the C<csv_cleanup_needed()> function.  Thanks, Jan!

=head1 AUTHOR

The author, Avinash Kak, recently finished a 17-year long "Objects Trilogy Project"
with the publication of the book I<Designing with Objects> by John-Wiley. If
interested, visit his web page at Purdue to find out what this project was all
about. You might like I<Designing with Objects> especially if you enjoyed reading
Harry Potter as a kid (or even as an adult, for that matter).