Decision Tree

Learning OpenCV book has a good description of the Decision Tree ML method. The API documentation also have a good summary.

Implementation
Based on paper by "Classification and Regression Trees" by Brieman et al.

Parameters

• Related to Tree Pruning (Post-Build)
•   use_1st_rule, truncate_prune_tree
• Is cv_fold use for both tree building and pruning with respect to average Gini error?
• Support surrogate splits to handle unknown data - meaning it finds 'backup' attributes from the feature vector that would split the node with similar 'purity'.

Sample (mushroom)

• Classification of mushrooms of being Poisonous or Edible based on 20 discrete attributes.
• Demonstrate decision tree traversal with interactive-prediction phase.
• Display a table of importance of attributes after the tree is built.
• Lots of data available to use from UC-Irvine ML Data Repository (see Resources)
• Sample could be easily modified to tackle other classification databases.
• Setting 'penalty-weight' to 1 gives 8 percent of false-negatives. Quickly decreased to 0 when it is set to 2.

Resources

• http://www.statsoft.com/textbook/classification-and-regression-trees/
• http://www.ics.uci.edu/~mlearn/MLRepository.html (Machine Learning Data Set Repository at UC-Irvine)

Readings

• Learning OpenCV, Gary Bradski & Adrian Kaebler (O'Reilly Press)
• Introduction to Machine Learning, 2nd Edition, Ethern Alpaydin (MIT Press)