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A machine learning-based predictive model design space exploration (DSE) method for high-level synthesis (HLS) is presented. The method creates a predictive model for a training set until a given error threshold is reached and then continues with the exploration using the predictive model avoiding time-consuming synthesis and simulations of new configurations. Results show that the authors' method is on average 1.92 times faster than a genetic-algorithm DSE method generating comparable results, whereas it achieves better results when constraining the DSE runtime. When compared with a previously developed simulated annealer (SA)-based method, the proposed method is on average 2.09 faster, although again achieving comparable results.