The empirical error equation given in the book 'Understanding machine language: from theory to algorithms'Understanding Machine Learning: From Theory to Algorithms is
My intuition for this equation is: total wrong predictions divided by the total number of samples m$m$ in the given sample set S$S$ (Correct me if I'm wrong). But, in this equation, the m$m$ takes {1,....,m}$\{ 1, \dots, m \}$. How is this actually calculated, as I thought it should be one number (the size of the sample)?