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In the context of credit scoring, one tries to develop a predictive model using a regression formula such as *Y* = Σ *w _{i} R_{i}*, where

- high dollar amount transaction
- high risk country
- high risk merchant category

This is the first order model. The second order model involves cross products *R _{i}* x

Note that when the weights are binary, this is a typical combinatorial optimization problem. When the weights are constrained to be linearly independent over the set of integer numbers, then each Σ *w _{i} R_{i}* (sometimes called unscaled score) corresponds to one unique combination of rules. It also uniquely represents a final node of the underlying decision tree defined by the rules.

**Contributions:**

- From Mark Hansen:
When the rules are binary, the problem is known as logic regression.

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