[MUSIC] When there's a trade off between
precision and recall, it's important for us to look at the two extremes. What does it mean to have a classifier
that's extremely precise? And what does it mean to have a classifier
that's extremely high recall? And how the two can go
against each other sometimes. First, let's think about what I
call an optimistic classifier. You might know some of these
optimists in your life. They think everything is good. How's it going? Good.
Even if bad stuff is happening, they say good. Those folks say that all
possible experiences are good, so they're optimists. That means that pretty much every input,
every sentence, is labeled as positive,
very few get labeled as negative. It's extremely likely that all the truly
positive data points get labeled as good. What does that mean? That means that I have perfect recall, because I recall all those
positive data points. Good. But I might not get perfect precision because I put in
a bunch of negatives into that bit. How can we address that? We can have that pessimistic classifier, you might have some friends like
that where you try really hard, you do everything for them, you go out
of your way, and everything sucks. Every single experience that
you have is really bad. There's very, very, very, very,
few things that they say are good. And when they are there very likely to be
good but everything else they say is bad, so everything else in the world
is very hard, equals -1. Pessimist means that you're going to
miss out on many good things in life. The pessimists have high precision because
the few things that was good tends to be good, but very, very, very low recall,
they don't inspire great things in life. It turns out there is a spectrum between
a high precision low recall model and low precision high recall model,
the pessimist and the optimist. What we'd like to do is somehow balance
between the two perspectives in the world to find something that's just right for
us. So, balance between a pessimistic
model and the optimistic model. In particular, we want to find as many
positive reviews or sentences as possible, as many of those as possible, with as
few incorrect predictions as we can. So, that's the balance we're trying to
strike in the case of our restaurant. [MUSIC]