System and Method for Generating Greedy Reason Codes for Computer Models

1 . A system for generating greedy reason codes for computer models, comprising:

a computer system for receiving and processing a computer model of a set of data, said computer model having at least one record scored by the model; and

a greedy reason code generation engine stored on the computer system which, when executed by the computer system, causes the computer system to:

identify reason code variables that explain why a record of the model is scored high by the model; and

build an approximate model to simulate a likelihood of a high score being generated by at least one of the reason code variables identified by the engine.

2 . The system of claim 1 , wherein the greedy reason code generation engine, when executed by the computer system, further causes the computer system to:

compute for each of a plurality of input variables a difference between an original score and a score without the input variable;

identify a first input variable that causes a maximum score drop when removed, and defining the first input variable as a backward variable;

score each record by keeping only the backward variable and each of the other input variables;

identify a second input variable associated with a highest score, and defining the second input variable as a forward variable;

combine the backward variable and the forward variable into a reason code; and

calculate total contribution of the reason code by computing a difference between an original score and a score without the reason code.

3 . The system of claim 2 , wherein a plurality of forward variables are identified and defined until a stopping criterion is met.

4 . The system of claim 3 , wherein the stopping criterion is when a total number of input variables is equal to a predefined number.

5 . The system of claim 3 , wherein the stopping criterion is when a score contributed by the backward variable and forward variables is above a threshold.

6 . The system of claim 1 , wherein the approximate model is a Gaussian Missing Data Model.

7 . A method for generating greedy reason codes for computer models comprising:

receiving and processing, by a computer system, a computer model of a set of data, said computer model having at least one record scored by the model;

identifying, by a greedy reason code generation engine stored on and executed by the computer system, reason code variables that explain why a record of the model is scored high by the model; and

building by the greedy reason code generation engine an approximate model to simulate a likelihood of a high score being generated by at least one of the reason code variables identified by the engine.

8 . The method of claim 7 , further comprising:

computing for each of a plurality of input variables a difference between an original score and a score without the input variable;

identifying a first input variable that causes a maximum score drop when removed, and defining the first input variable as a backward variable;

scoring each record by keeping only the backward variable and each of the other input variables;

identifying a second input variable associated with a highest score, and defining the second input variable as a forward variable;

combining the backward variable and the forward variable into a reason code; and

calculating total contribution of the reason code by computing a difference between an original score and a score without the reason code.

9 . The method of claim 8 , wherein a plurality of forward variables are identified and defined until a stopping criterion is met.

10 . The method of claim 8 , wherein the stopping criterion is when a total number of input variables is equal to a predefined number.

11 . The method of claim 8 , wherein the stopping criterion is when a score contributed by the backward variable and forward variables is above a threshold.

12 . The method of claim 7 , wherein the approximate model is a Gaussian Missing Data Model.

13 . A non-transitory computer-readable medium having computer-readable instructions stored thereon which, when executed by a computer system, cause the computer system to perform the steps of:

receiving and processing, by the computer system, a computer model of a set of data, said computer model having at least one record scored by the model;

identifying, by a greedy reason code generation engine stored on and executed by the computer system, reason code variables that explain why a record of the model is scored high by the model; and

building by the greedy reason code generation engine an approximate model to simulate a likelihood of a high score being generated by at least one of the reason code variables identified by the engine.

14 . The computer-readable medium of claim 13 , further comprising:

computing for each of a plurality of input variables a difference between an original score and a score without the input variable;

identifying a first input variable that causes a maximum score drop when removed, and defining the first input variable as a backward variable;

scoring each record by keeping only the backward variable and each of the other input variables;

identifying a second input variable associated with a highest score, and defining the second input variable as a forward variable;

combining the backward variable and the forward variable into a reason code; and

calculating total contribution of the reason code by computing a difference between an original score and a score without the reason code.

15 . The computer-readable medium of claim 14 , wherein a plurality of forward variables are identified and defined until a stopping criterion is met.

16 . The computer-readable medium of claim 14 , wherein the stopping criterion is when a total number of input variables is equal to a predefined number.

17 . The computer-readable medium of claim 14 , wherein the stopping criterion is when a score contributed by the backward variable and forward variables is above a threshold.

18 . The computer-readable medium of claim 13 , wherein the approximate model is a Gaussian Missing Data Model.