[MUSIC] Throughout this course we have
evaluated classifiers in one key way. We measured error or
the accuracy of that classifier. But it turns out that for
many real world applications, error or accuracy is not great
measure to try to understand whether classifier is doing
the right thing for you. And in this module, we're going to
talk about precision recall, which is a really cool, very simple way
to evaluate classifiers that captures something that's needed for
a wide range of applications. And we'll use a cool, fun application as a kind of running
example throughout the module. So here's the idea. Let's say I have a restaurant and
I have a goal. I want to increase the number of guests, the number of people,
coming to my restaurant by 30%. And I say, I'm going to do a cool
advertising campaign to do that. But nobody wants to just get
those ads in the mail or spam email as their advertising campaign. So I want to be innovative, I want to be
authentic about my advertising campaign. And the way that I want to be authentic is
that when I use the voice of my customers to talk about how great the restaurant is. So when I'm looking at customer reviews,
and then I find great things in there, great nuggets, to be able to tell everyone
about how great my restaurant is. So I want to find great quotes, key positive sentences that describe
amazing things about my restaurant. And may even find some spokespeople
that are really eloquent, they explain really well what
they love about my restaurant. So that's my goal. And so, I might do that automatically
by taking the restaurant reviews and posting key sentences or key things
that people are saying on my website. So that anybody who lands there can see
the latest greatest of what's being said. So I want to do a lot of
these restaurant reviews, and I want to automate a lot of that process. So I want to do it. So just like we described in the first
course in this specialization, we're going to take the restaurant reviews, and
we're going to split them into sentences. So for every sentence,
some are positive about my restaurant, some are negative,
I want to be able to evaluate. So for example, if the sentence says, easily the best sushi in Seattle,
that's my input xi. And when I feed that for
the sentence sentiment classifier and get an output y hat I which is either
positive sentiment or negative sentiment. And I want to do that for
every sentence in every review that I get. And, in fact,
I might even do that in real time. Every time a new review comes in, I'm
going to feed it through this classifier model, and start to predict y hat i
has been positive for some sentences. These are the great ones,
the ones I want to show off, the ones I want to talk about. And then y hat i -1, this is the negative
ones, I want to ignore those. So I only care about the positive ones,
and I'm going to do my best to
take those positive sentences, show them in a way that people really
feel, man, my restaurant is awesome. I'm going to go there for sushi. So how do I find those positive sentences? I am going to do a sentiment classifier. How do I know the sentiment classifier
is really good, I can trust it, I can put those sentences on my
website without having to check every time a sentence goes up? This is the key point. We are talking about
automating machine learning. You have to really trust
the machine learning model. So if I give you a particular, say,
accuracy, is that enough trust for me to just automatically feed reviews into
something that shows up in my website? [MUSIC]