Transforming predictive models

摘要

According to one embodiment of the present disclosure, 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.

主权项

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 microprocessor, 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 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; and creating, using the processor, a linear model based on the variable name, the cutoff point, and the combined weight.