We present a machine learning approach to the problem of RF specification test compaction. The proposed compaction flow relies on a multi-objective genetic algorithm, which searches in the power-set of specification tests to select appropriate subsets, and a classifier, which makes pass/fail decisions based solely on these subsets. The method is demonstrated on production test data from an RF device fabricated by IBM. The results indicate that machine learning can identify intricate correlations between specification tests, which allows us to infer the outcome of all tests from a subset of tests. Thereby, the number of tests that need to be explicitly carried out and the corresponding cost are reduced significantly without adversely impacting test accuracy.