The function of an automatic feedback system in machine learning is to continuously evaluate and improve the performance of a model by providing real-time feedback based on its output. This system monitors predictions against actual results, allowing for the identification of discrepancies or errors. By analyzing this feedback, the machine learning algorithm can adapt and refine its parameters, enhancing its accuracy over time. This iterative process is critical for building robust models that learn from data, enabling them to generalize better and perform effectively across diverse datasets and situations, ultimately improving overall predictive performance.