Hidden decision trees (HDT) is a technique patented by Dr. Granville, to score large volumes of transaction data. It blends robust logistic regression with hundreds small decision trees (each one representing for instance a specific type of fraudulent transaction) and offers significant advantages over both logistic regression and decision trees: robustness, ease of interpretation, and no tree pruning, no node splitting criteria. It makes this methodology powerful and easy to implement even for someone with no statistical background.
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-linearity and strongly correlated independent variables.
The technique is easy to implement in any programming language. It is more robust than decision trees or logistic regression, and helps detect natural final nodes. 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 a 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. The technique is not connected with hidden Markov fields.
- Fraud detection, spam detection
- Web analytics
- Keyword scoring/bidding (ad networks, paid search)
- Transaction scoring (click, impression, conversion, action)
- Click fraud detection
- Web site scoring, ad scoring, landing page / advertiser scoring
- Collective filtering (social network analytics)
- Relevancy algorithms
- Text mining
- Scoring and ranking algorithms
- Infringement detection
- User feedback: automated clustering
The model presented here is used in the context of click scoring. The purpose is to create predictive scores, where score = f(response), that is, score is a function of the response. The response is sometimes referred to as the dependent variable in statistical and predictive models.
- Examples of Response:
- Odds of converting (Internet traffic data – hard/soft conversions)
- CR (conversion rate)
- Probability that transaction is fraudulent
- Independent variables: Called features or rules. They are highly correlated