# Empirical Risk Minimization

In Machine Learning, the training set error is often called the empirical error or empirical risk, and this is the error the classifier incurs over the sample:

$$L_S(h) = \frac{|\{i \in [m] : h(x_i) \ne y_i\}}{m}$$

Given a hypothesis class $$H$$, finding the hypothesis $$h \in H$$ that minimizes the empirical risk is a simple learning strategy.