Jethro's Braindump

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:

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

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

Links to this note