Until now, we have just looked at regression settings, that is, problems where our main problem is to predict the level or value of a given response variable, starting from a set of explanatory variables. As you know, there are also classification problems, which are problems where you want to assign your observation to one of a given set of categories.
How do we measure the performance of these models? As always, you just have to resonate about the objective of your model to understand how to measure its performance. Our classification model aims to assign each observation to its category. How can you tell if it's doing well? You would probably count how many times it meets its objective, that is, how many correct classifications it performs.
This is actually one of the most common ways to a measure classification models' performance, even with some further development. Let's see it a bit closer.