1. A computer-implemented method comprising: 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; generating a trained predictive model using a first subset of the initial training data and a training function; generating intermediate training records including inputting input data of a second subset of the initial training records to the trained predictive model, each intermediate training record having a score; and generating a score normalization model using a score normalization training function and the intermediate training records.
1. A computer-implemented method comprising: 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; generating a trained predictive model using a first subset of the initial training data and a training function; generating intermediate training records including inputting input data of a second subset of the initial training records to the trained predictive model, each intermediate training record having a score; and generating a score normalization model using a score normalization training function and the intermediate training records. 2. The method of claim 1 , further comprising: dividing the initial training records into sets of training records, the sets of training records including a first set of training records; generating a second trained predictive model using at least one of the sets of training records; and generating an intermediate training record for each initial training record in the first set of training records, each intermediate training record having a value as input and the category of a respective initial training record as output, wherein the value is generated by inputting input data of the respective initial training record into the second trained predictive model.