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And now we're eventually
at the final round

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of our logistic regression model,
which is the fun part, because we're going

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to visualize the training set results
as well as the test set results.

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And basically, we're going to make a plot
that will represent

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everything that is happening
with our logistic regression model.

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So let's make this chart.

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I'm going to do like in Python.

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I'm going to take the code
that I've prepared and paste it here.

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I will select and execute the code.

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And then for those of you
who are interested

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in the idea behind the code,
that is how the code plots the graph.

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I will explain how the code works
at the end of this tutorial.

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Okay, so right now
I'm going to paste the code.

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Here we go.

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Then this code contains 15 lines of code.

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That was also the case in Python.

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And that's because it's the exact
same code based on the same idea.

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And I will explain this idea.

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That means I will explain how this code
works at the end of this tutorial.

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But for now let's select
this code and let's see what happens.

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So command and Control
plus enter to execute.

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And let's wait.

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It's taking a little time. That's normal.

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But it's going to plot something.

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There it is.

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There it is. That's the plot.

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Okay.

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So for those of you

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who follow the Python tutorial,
we obtain exactly the same plot.

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So let's describe this plot.

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I'm going to enlarge this.

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Okay.

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So let's analyze this graph step by step.

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First let's focus on all the points. Here.

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We can see that we have some red points.

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And some green points.

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So all these points that we see on this
graph

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are the observation
points of our training set.

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That is these are all the users
of the social network

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that were selected
to go to the training set.

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And each of these users
here is characterized by its age.

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Here on the x axis and its estimated
salary here on the y axis.

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Now we can see that
there are some red points here

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and some green points here.

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The red points are the training set
observations for which the dependent

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variable purchased is equal to zero,
and the green points are the training set.

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Observations for which the dependent
variable purchased is equal to one.

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That means that the red points here
are the users

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who didn't buy the SUV,
and the green points.

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Here are the users who bought
who actually bought the SUV.

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So now, as a first step of analysis,
let's give an interpretation

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of what we observe here with these users.

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Okay.

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So first we can see that the users
who are young with a low estimated salary.

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So these users here
actually didn't buy the SUV.

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Then if we look at the users who are older
and with a higher estimated salary,

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well, we can see that most of these users
actually bought the SUV.

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And it actually makes sense
because the SUV is more like a family car

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and therefore more interesting

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for these older users here
with a high estimated salary.

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Besides,
we can also see that some older people,

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even with a low estimated salary,
actually bought the SUV,

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because we can see that
we have some green points here

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that correspond to an age
above the average.

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The average is here,

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but an estimated salary below the average
because the average is here.

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Okay.

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So these guys, these older guys,
although they have a low estimated salary,

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actually bought the SUV, probably
because they've been saving up some money.

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Or maybe they finished
paying of their mortgage.

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I don't know, but what's for sure
is that they couldn't resist buying this

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very cool luxury
SUV offered at a ridiculously low price.

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And on the other

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hand, we can also see that
there are some young people here

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with a high estimated salary
who actually bought the SUV.

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You know, maybe
because it's a very cool SUV

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and they want to impress their friends
and take them into road trips.

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Or maybe they already have a family,
I don't know.

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Anyway, they bought the SUV.

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Actually, there are a lot of buyers,
so this must be a very cool and cheap SUV.

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Okay, and now
what is the goal of classification?

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Now we're talking machine learning.

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Why are we making some classifiers?

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And what will classifiers do.

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And least what are we trying to make them
do for this particular business problem.

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Well the goal here is to classify

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the right users into the right categories.

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That is,
we are trying to make a classifier

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that will catch the right users
into the right category,

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which are yes, they buy the SUV and no,
they don't buy the SUV.

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And we represented the way
our classifier catches these users

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by plotting
what I called the prediction regions.

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And so the prediction regions are
the two regions that we see on this graph.

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This red one here and this green one here.

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And the red prediction region
is the region where our classifier catches

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all the users that don't buy the SUV.

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And the green prediction region

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is the region where a classifier catches
all the users that buy the SUV.

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But be careful.

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This is according to the classifier
that is for each user of this

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red prediction region here.

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Our logistic regression
classifier predicts that the user doesn't

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buy the SUV.

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And for each user of this green prediction
region here,

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our classifier will predict
that the user buys the SUV.

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So that makes an awesome tool
because for each

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new user of the social network,
well, a logistic regression

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classifier will tell based on its age
and its estimated salary.

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If this user belongs to this
red prediction region here,

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and therefore doesn't buy the SUV,
or if this user belongs

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to this green prediction
region here and therefore buys the SUV.