Transforming predictive models

Patent metadata

Field	Value
Publication number	US-9280740-B1
Application number	US-201514594523-A
Country	US
Kind code	B1
Filing date	Jan 12, 2015
Priority date	Jan 12, 2015
Publication date	Mar 8, 2016
Grant date	Mar 8, 2016

Abstract

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.

First claim

What is claimed is:

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.

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 , further comprising creating a performance scorecard based on the combined weight for the one-level, binary split-node variables.

4. The method of claim 1 , further comprising creating a model evaluation based on an error rate of the trained boosting model.

5. The method of claim 1 , further comprising creating a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model.

6. The method of claim 1 , wherein the linear model comprises conditional logic.

7. The method of claim 1 , wherein the linear model is in an if-then-else format.

8. 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; and
create a linear model based on the variable name, the cutoff point, and the combined weight.

9. The non-transitory media of claim 8 , wherein the trained boosting model is one selected from the group comprising: Discrete Adaboost, Real Adaboost, Gentle Adaboost, and Logitboost.

10. The non-transitory media of claim 8 , further comprising creating a performance scorecard based on the combined weight for the one-level, binary split-node variables.

11. The non-transitory media of claim 8 , further comprising creating a model evaluation based on the error rate of the trained boosting model.

12. The non-transitory media of claim 8 , further comprising creating a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model.

13. The non-transitory media of claim 8 , wherein the linear model comprises conditional logic.

14. The non-transitory media of claim 8 , wherein the linear model is in an if-then-else format.

15. 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; and
create a linear model based on the variable name, the cutoff point, and the combined weight.

16. The system of claim 15 , wherein the trained boosting model is one selected from the group comprising: Discrete Adaboost, Real Adaboost, Gentle Adaboost, and Logitboost.

17. The system of claim 15 , further comprising creating a performance scorecard based on the combined weight for the one-level, binary split-node variables.

18. The system of claim 15 , wherein the processor is further operable to create model evaluation based on the error rate of the trained boosting model.

19. The system of claim 15 , wherein the processor is further operable to create a model evaluation based on a Kolmogorov-Smirnov test of the trained boosting model.

20. The system of claim 15 , wherein the linear model comprises conditional logic.

Assignees

• Bank Of America

Inventors

• Laxmanan Kasilingam Basker
• Chen Yudong
• Song Peng

Classifications

• G06F30/00

  Computer-aided design [CAD] · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06N99/005

  Physics · mapped topic