Disclosed embodiments include methods and systems for classifying test data. In one embodiment a method includes determining one or more variable types in a multivariate test vector within a data set, and for a plurality of machine -learning models, determining a closest match between variable types used by (to train) the machine -learning models and the determined variable types for the test vector. In response to determining a closest match for one machine-learning model, a corresponding machine -learning model is selected and the test vector is classified using the selected model. In response to determining a closest match for multiple machine -learning models, a similarity is determined between a probability distribution for the test data set and the probability distributions for the multiple machine- learning models to generate similarity values for each of the models. In response to one of the similarity values exceeding a threshold value, a machine-learning model is selected that corresponds to the exceeding similarity value and the test vector is classified using the selected model.
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