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We show how interpolants can be viewed as classifiers in supervised machine learning. This view has several advantages: First, we are able to use off-the-shelf classification techniques, in particular support vector machines (SVMs), for interpolation. Second, we show that SVMs can find relevant predicates for a number of benchmarks. Since classification algorithms are predictive, the interpolants computed via classification are likely to be invariants. Finally, the machine learning view also enables us to handle superficial non-linearities. Even if the underlying problem structure is linear, the symbolic constraints can give an impression that we are solving a non-linear problem. Since learning algorithms try to mine the underlying structure directly, we can discover the linear structure for such problems. We demonstrate the feasibility of our approach via experiments over benchmarks from various papers on …