2009-03-21 19:27:43 UTC
like logistic regression, SVM, neural networks or decision trees) to
handle problems with large amounts of data, non-linearities and strongly
correlated dependent variables.
The technique is easy to implement in any programming language. It is more
robust than decision trees or logistic regression. Implementations
typically rely heavily on large, granular hash tables.
No decision tree is actually built (thus the name hidden decision trees),
but the final output of an hidden decision tree procedure consists of a
few hundred nodes from multiple non-overlapping small decision trees. Each
of these parent (invisible) decision trees corresponds e.g. to a
particular type of fraud, in fraud detection models. Interpretation is
straightforward, in contrast with traditional decision trees.
Please share your thoughts, how could this be implemented in SAS, or read
comments at http://www.analyticbridge.com/profiles/blogs/hidden-decision-