Empirical Risk Minimization

Empirical Risk Minimization (ERM) is a method used in machine learning to make predictions more accurate by focusing on minimizing errors on known data. It involves analyzing a dataset, measuring how well the model's predictions match the actual outcomes, and adjusting the model to reduce those errors. Essentially, ERM trains the model to perform as accurately as possible on the data it has seen, with the goal that it will also do well on new, unseen data. This approach helps create reliable models for tasks like recognizing images or predicting trends.