A system for translating a boosting algorithm includes an interface communicatively coupled to a processor. The interface is operable to receive a trained boosting model. The processor is operable to identify a plurality of split-node variables associated with the trained boosting model. Each of the plurality of split-node variables comprises a variable name, a cutoff point, and a weight. The processor may aggregate the split-node variables by variable name and cutoff point and then combine the weights of each of the plurality of split-node variables having the same variable name and cutoff point. The processor may then create a linear model based on the combined variables. The processor may further create a performance scorecard based on the combined weight, and create a model evaluation based on the error rate of the trained boosting model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for translating a boosting algorithm, comprising: receiving, at a hardware interface, a trained boosting model; identifying, using a processor communicatively coupled to the interface, a plurality of one-level, binary split-node variables associated with the trained boosting model, wherein each of the plurality of one-level, binary split-node variables comprises a variable name, a cutoff point, and a weight; identifying, using the processor, a group of one-level, binary split-node variables that have the same variable name and cutoff point within the plurality of one-level, binary split-node variables; combining, using the processor, the weights of each of the one-level, binary split-node variables in the group of one-level, binary split-node variables to calculate a combined weight for the one-level, binary split-node variables in the group of one-level, binary split-node variables, wherein combining the weights comprises summing the weights of the one-level, binary split-node variables in the group of one-level, binary split-node variables; creating, using the processor, a linear model based at least in part upon the variable name, the cutoff point, and the combined weight; creating a performance scorecard based on the combined weight for the group of one-level, binary split-node variables; and creating a model evaluation based on an error rate of the trained boosting model. 2. The method of claim 1, wherein the trained boosting model is one selected from the group comprising: Discrete Adaboost, Real Adaboost, Gentle Adaboost, and Logitboost. 3. The method of claim 1, wherein the linear model comprises conditional logic. 4. The method of claim 1, wherein the linear model is in an if-then-else format. 5. The method of claim 1, further comprising creating a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model. 6. A non-transitory computer readable storage medium comprising logic, the logic operable, when executed by a processor, to: receive a trained boosting model; identify a plurality of one-level, binary split-node variables associated with the trained boosting model, wherein each of the plurality of one-level, binary split-node variables comprise a variable name, a cutoff point, and a weight; identify a group of one-level, binary split-node variables that have the same variable name and cutoff point within the plurality of one-level, binary split-node variables; combine the weights of each of the one-level, binary split-node variables in the group of one-level, binary split-node variables to calculate a combined weight for the one-level, binary split node variables in the group of one-level, binary split node variables, wherein combining the weights comprises summing the weights of the one-level, binary split-node variables in the group of one-level, binary split-node variables; create a linear model based on the variable name, the cutoff point, and the combined weight; creating a performance scorecard based on the combined weight for the one-level, binary split-node variables; and creating a model evaluation based on the error rate of the trained boosting model. 7. The non-transitory media of claim 6, wherein the trained boosting model is one selected from the group comprising: Discrete Adaboost, Real Adaboost, Gentle Adaboost, and Logitboost. 8. The non-transitory media of claim 6, wherein the linear model comprises conditional logic. 9. The non-transitory media of claim 6, wherein the linear model is in an if-then-else format. 10. The non-transitory media of claim 6, further comprising creating a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model. 11. A system for translating a boosting algorithm, comprising: a hardware interface operable to: receive a trained boosting model; and a hardware processor operable to: identify a plurality of one-level, binary split-node variables associated with the trained boosting model, wherein each of the plurality of one-level, binary split-node variables comprises a variable name, a cutoff point, and a weight; identify a group of one-level, binary split-node variables that have the same variable name and cutoff point within the plurality of one-level, binary split-node variables; combine the weights of each of the one-level, binary split-node variables in the group of one-level, binary split-node variables to calculate a combined weight for the one-level, binary split-node variables in the group of one-level, binary split-node variables, wherein combining the weights comprises summing the weights of the one-level, binary split-node variables in the group of one-level, binary split-node variables; create a linear model based on the variable name, the cutoff point, and the combined weight; creating a performance scorecard based on the combined weight for the one-level, binary split-node variables; and create a model evaluation based on the error rate of the trained boosting model. 12. The system of claim 11, wherein the trained boosting model is one selected from the group comprising: Discrete Adaboost, Real Adaboost, Gentle Adaboost, and Logitboost. 13. The system of claim 11, wherein the processor is further operable to create a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model. 14. The system of claim 11, wherein the linear model comprises conditional logic.