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Polar radio architectures are attractive due to the ability to implement them using largely digital architectures. However, testing for specs such as EVM incurs significant test time due to the large numbers of symbols that need to be transmitted. In our approach, EVM is modeled as a function of the system static non-idealities (IQ mismatch, gain, IIP3 parameters) and dynamic non-idealities (VCO phase noise). Using a multi-tone test stimulus, the static and dynamic non-idealities are estimated first. The data generated is used to predict EVM using machine learning methods with high accuracy while incurring minimal test time.