Hidden Decision Trees is a statistical and data mining methodology (just 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.
Hidden Decision Trees is a statistical and data mining methodology (just 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.
The methodology was first invented in the context of credit card fraud detection, back in 2003. It is not implemented in any statistical package at this time. Frequently, hidden decision trees are combined with logistic regression in an hybrid scoring algorithm, where 80% of the transactions are scored via hidden decision trees, while the remaining 20% are scored using a compatible logistic regression type of scoring.
Hidden decision trees take advantage of the structure of large multivariate features typically observed when scoring a large number of transactions, e.g. for fraud detection.
