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Machine learning has recently been applied and deployed at several light source facilities in the domain of accelerator physics. Here, an approach based on machine learning to produce a fast-executing model is introduced that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning model can be trained on simulated data and later calibrated to a smaller, limited measured data set, a technique referred to as transfer learning. This result will enable users to efficiently determine the insertion device settings for achieving arbitrary beam characteristics.