True or False: Empirical Risk Minimization (ERM) guarantees that the model will perform perfectly on unseen data by solely focusing on minimizing the average loss over the training dataset.

False. Empirical Risk Minimization (ERM) only looks at the training data and tries to make the loss small there, but it does not control how the model behaves on data it has never seen. If the training set is small, noisy, or not representative, the model can fit the training points well yet perform badly on new examples, a problem known as overfitting. ERM guarantees low empirical risk, but generalization requires additional assumptions or techniques such as regularization or bounds on model complexity. For instance, a neural network that memorizes every training image can achieve near‑zero training error while misclassifying most new images, showing that ERM alone does not ensure perfect unseen performance.