2
$\begingroup$

The empirical error equation given in the book Understanding Machine Learning: From Theory to Algorithms is

enter image description here

My intuition for this equation is: total wrong predictions divided by the total number of samples $m$ in the given sample set $S$ (Correct me if I'm wrong). But, in this equation, the $m$ takes $\{ 1, \dots, m \}$. How is this actually calculated, as I thought it should be one number (the size of the sample)?

Improve this question
$\endgroup$

1 Answer 1

Reset to default
2
$\begingroup$

This is a commonly used notation in theoretical computer science.

$[m]$ is not the variable $m$, but is instead the set of integers from $1$ to $m$ inclusive. The empirical error equation thus reads in English:

The cardinality of a set consisting of the elements $i$ of the set of integers $[m]$ such that the hypothesis given input $x_i$ disagrees with label $y_i$, normalized by $m$.

Improve this answer
$\endgroup$

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .