There are two basic approaches to choosing f {\displaystyle f} or g {\displaystyle g} : empirical risk minimization and structural risk minimization.[6] Empirical risk minimization seeks the function that best fits the training data. Structural risk minimize includes a penalty function that controls the bias/variance tradeoff.