# What is the difference between a “learner” and “classifier” in supervised learning?

This question stems from Pedro Domingos' excellent paper "A Few Useful Things to Know About Machine Learning." The paper is extremely clear and well-written, but I still have a clarification question. Namely, what is the difference between what Domingos describes on page 1 as a "learner" and a "classifier"? I have largely taken these to by synonymous. The sentence that threw me for a loop in the paper is (again, on page 1): "The test of the learner is whether this classifier produces the correct output yt for future examples xt." I thought this was simply the test of the classifier. Any clarification or further reading suggestions would be greatly appreciated.

An alternative way to understand this is that a learner takes the input $x_1,x_2,..,x_p,y$ and produces a classifier. A classifier takes as input $x'_1,x'_2,..,x'_p$ and produces $y'$.