1. A computer-implemented method of selecting a classifier for production, the method comprising: identifying a plurality of classifiers; selecting a set of test cases based on time; grouping the set of test cases into a plurality of datasets based on time, each of the plurality of datasets associated with a different interval of time; applying each of the plurality of classifiers to each of the plurality of datasets to generate classifications for test cases in each of the plurality of datasets; determining, for each of the plurality of classifiers, a classification performance score for each of the plurality of datasets based on the classifications generated for the test cases of each dataset; determining, for each of the plurality of classifiers, a variance across the classification performance scores for the plurality of data sets; and selecting a classifier from among the plurality of classifiers for production, the selected classifier having a least amount of variance across the classification performance scores for the plurality of datasets associated with the different intervals of time.