madlib/madlib — pull request #134 by aojwang (2012-04-28 06:21:30+00:00)
Title: Random Forest Implementation For MADlib

Random forest (RF) is an ensemble classifier that consists of multiple decision trees (DTs) and outputs the class that is voted by the majority of the individual trees. A prominent advantage of RF is its unexcelled accurary. This is the main reason why we support RF in MADlib. MADlib RF is based on Leo Breiman and Adele Cutler’s RF algorithm ([4],[5] and [6]) and runs directly within databases.

Our RF implementation is based on MADlib DT without error based pruning and reduced error pruning. But we add a parameter, named max_split_point, to control how many feature values will be treated as split values. Using this approach, in most cases, the total time of training an RF can be decreased a lot.

Our RF functionality can be described as following.
* Training (rf_train)
* Displaying (rf_display)
* Classification (rf_classify)
* Scoring (rf_score)
* Cleaning (rf_clean)