11. A computer program product comprising a non-transitory computer-readable storage medium including executable code for selecting a classifier for production, the code when executed performs steps 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.
11. A computer program product comprising a non-transitory computer-readable storage medium including executable code for selecting a classifier for production, the code when executed performs steps 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. 14. The computer program product of claim 11 , wherein determining the classification performance score of each of the plurality of classifiers comprises: identifying, for the plurality of datasets, a plurality of first labels for the test cases in each dataset, each first label describing an accurate classification of a corresponding test case as being associated with a first classification or a second classification; comparing, for each of the plurality of classifiers, the generated classifications of test cases for each of the plurality of datasets with the plurality of first labels corresponding to the test cases in each of the plurality of datasets; and determining, for each of the plurality of classifiers, a classification performance score associated with each of the plurality of datasets based on the comparison.