Changes for version 0.03 - 2002-09-02
- Added a 'prune' parameter to new(), which controls whether the tree will be pruned after training. This is usually a good idea, so the default is to prune. Currently we prune using a simple minimum-description-length criterion.
- Training instances are now represented using a C struct rather than a Perl hash. This can dramatically reduce memory usage, though it doesn't have much effect on speed. Note that Inline.pm is now required.
- The list of instances is now deleted after training, since it's no longer needed.
- Small speedup to the train() method, achieved by less copying of data.
- If get_result() is called in a list context, it now returns a list containing the assigned result, a "confidence" score (tentative, subject to change), and the tree depth of the leaf this instance ended up at.
- Internally, each node in the tree now contains information about how many training examples contributed to training this node, and what the distribution of their classes was.
- Added an as_graphviz() method, which will help visualize trees. They're not terribly pretty graphviz objects yet, but they're visual.
Modules
Automatically Learns Decision Trees
C-struct wrapper for training instances