Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. A computer-implemented method comprising: receiving a predictive request; identifying a predictive model based on the predictive request; generating a result using the predictive model and the predictive request; and generating a normalized result using a score normalization model and the result, the score normalization model being generated using intermediate training records output by the predictive model and intermediate training records output by one or more additional predictive models. 3. The method of claim 2, wherein the predictive model is trained using a first subset of a set of initial training data and each of the one or more additional predictive models is trained using a respective second subset of the initial training records. 4. The method of claim 3, wherein the intermediate training records include: first intermediate training records generated by inputting the second subset of the initial training records for at least one of the one or more additional predictive models to the predictive model; and second intermediate training records generated by inputting the first subset of the initial training records to the at least one additional predictive model. 5. The method of claim 2, further comprising updating the predictive model in response to receiving additional training data, including training the predictive model using the additional training data and a different training function than a training function previously used to train the predictive model. 6. The method of claim 2, wherein the predictive model is trained using a first training function and at least one of the one or more additional predictive models is trained using a second training function different from the first training function. 7. The method of claim 2, wherein identifying a predictive model based on the predictive request comprise identifying the predictive model based on a score for the predictive model and a respective score for each of the one or more additional predictive models, the score for each predictive model being based on a measure of effectiveness of the predictive model. 8. The method of claim 2, wherein the predictive request includes input data that is input to the predictive model to generate the result. 9. A system, comprising: a data processing apparatus; and a memory storage apparatus in data communication with the data processing apparatus, the memory storage apparatus storing instructions executable by the data processing apparatus and that upon such execution cause the data processing apparatus to perform operations comprising: receiving a predictive request; identifying a predictive model based on the predictive request; generating a result using the predictive model and the predictive request; and generating a normalized result using a score normalization model and the result, the score normalization model being generated using intermediate training records output by the predictive model and intermediate training records output by one or more additional predictive models. 10. The system of claim 9, wherein the predictive model is trained using a first subset of a set of initial training data and each of the one or more additional predictive models is trained using a respective second subset of the initial training records. 11. The system of claim 10, wherein the intermediate training records include: first intermediate training records generated by inputting the second subset of the initial training records for at least one of the one or more additional predictive models to the predictive model; and second intermediate training records generated by inputting the first subset of the initial training records to the at least one additional predictive model. 12. The system of claim 9, wherein the operations further comprise updating the predictive model in response to receiving additional training data, including training the predictive model using the additional training data and a different training function than a training function previously used to train the predictive model. 13. The system of claim 9, wherein the predictive model is trained using a first training function and at least one of the one or more additional predictive models is trained using a second training function different from the first training function. 14. The system of claim 9, wherein identifying a predictive model based on the predictive request comprise identifying the predictive model based on a score for the predictive model and a respective score for each of the one or more additional predictive models, the score for each predictive model being based on a measure of effectiveness of the predictive model. 15. The system of claim 9, wherein the predictive request includes input data that is input to the predictive model to generate the result. 16. A non-transitory computer-readable storage medium encoded with a computer program, the program comprising instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a predictive request; identifying a predictive model based on the predictive request; generating a result using the predictive model and the predictive request; and generating a normalized result using a score normalization model and the result, the score normalization model being generated using intermediate training records output by the predictive model and intermediate training records output by one or more additional predictive models. 17. The non-transitory computer-readable storage medium of claim 16, wherein the predictive model is trained using a first subset of a set of initial training data and each of the one or more additional predictive models is trained using a respective second subset of the initial training records. 18. The non-transitory computer-readable storage medium of claim 16, wherein the intermediate training records include: first intermediate training records generated by inputting the second subset of the initial training records for at least one of the one or more additional predictive models to the predictive model; and second intermediate training records generated by inputting the first subset of the initial training records to the at least one additional predictive model. 19. The non-transitory computer-readable storage medium of claim 16, wherein the operations further comprise updating the predictive model in response to receiving additional training data, including training the predictive model using the additional training data and a different training function than a training function previously used to train the predictive model. 20. The non-transitory computer-readable storage medium of claim 16, wherein the predictive model is trained using a first training function and at least one of the one or more additional predictive models is trained using a second training function different from the first training function. 21. The non-transitory computer-readable storage medium of claim 16, wherein identifying a predictive model based on the predictive request comprise identifying the predictive model based on a score for the predictive model and a respective score for each of the one or more additional predictive models, the score for each predictive model being based on a measure of effectiveness of the predictive model.