4. The computer-implemented method of claim 1 , wherein grouping the set of test cases comprises: dividing a period of time associated with the set of test cases into a plurality of different time intervals, each of the plurality of different time intervals associated with one of the plurality of datasets; assigning each of the plurality of test cases to one of the plurality of datasets based at least in part on whether the time in which the test case was initially received for classification is within the time interval associated with the dataset.
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.