[MUSIC] So we've talked about using k-NN for
regression, but these methods can also be very, very
straightforwardly used for classification. So this is a little warm-up for
the next course in this specialization, which is our classification course. And let's start out by just
recalling our classification task, where we're gonna do this in
the context of spam filtering. Where we have some email as our input, and the output is gonna be whether
the email is spam or not spam. And we're gonna make this decision
based on the text of the email. Maybe information about the sender,
IP and things like this. Well, what we can do is use k-NN for
classification. Visually we can think about just taking
all of the emails that we have labeled as spam or not spam and
throwing them down in some space. Where the distance between
emails in this space represents how similar the text or
the sender IP information is. All the inputs or the features we're
using to represent these emails. And then what we can do,
is we get some query email that comes in. So, that's this little gray email here. And we're gonna say,
is it spam or not spam? There's a very intuitive way to do this,
which is just search for the nearest neighbors of this email. The emails most similar to this email. And then we're just gonna
do a majority vote on the nearest neighbors to decide whether
this email is spam or not spam. And what we see in this case, is that
four of the neighbors are spam, and only one neighbor is not spam, so
we're gonna label this email as spam. And so this is the really, really
straightforward approach of using k-NN for classification. [MUSIC]