A system and method for generating greedy reason codes for computer models is provided. The 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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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.