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Hello and welcome to this art tutorial.

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So in a previous tutorial,
we just pre-processed our data set to make

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it ready for our logistic regression model
that we will fit on the training set.

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So that's what
we're going to do in this tutorial.

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And let's do it right now.

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So it's going to be very simple.

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We're just going to create our classifier

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equals.

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And then we're going to use an existing
function of R called the glm function.

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That is going to build this
logistic regression classifier.

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So let's type here glm.

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And then let's press F1
to see what parameters we have to input.

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Okay.

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So the first thing we see is
that GLM is for generalized linear models.

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And that's because the logistic regression
is a linear classifier.

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For those of you
who didn't follow the Python tutorial,

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you will see that our logistic regression
classifier

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will linearly separate
our two classes of users.

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And then let's look at the arguments
to see why we have two inputs.

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All right
so the first argument is formula.

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So that's the same as what we did
in the regression section here.

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We're going to write formula

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equals.

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And then we take the dependent variable
which is purchased.

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Then alt plus n.

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And then here a dot to specify
that we want to take all

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the independent variables.

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So that's way
R understands that we want to predict

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the purchased result
based on all the independent variables

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which are the age
and the estimated salary.

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All right.

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Now what is the next argument.

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So let's add a comma here. Enter.

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And let's add the second argument.

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So the second argument is family.

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And here since we are in logistic
regression we're going to add family

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equals binomial.

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So at this stage it's

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not important to understand
what binomial means exactly.

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But you just have to remember that

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for logistic regression
you have to specify the binomial family.

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Here.

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And then
we just need to input one last argument.

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It's the data here

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which is the data on which you want to
train your logistic regression model.

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So of course that's our training set.

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So we're going to add here
data equals training set.

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Here we go. Here it is. Okay.

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And that's it.

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By only using this GLM function
and precisely the formula the family

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and the fact that we're building the model
and the training set

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that build
our logistic regression classifier.

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So now let's select this

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command and control plus enter to execute.

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And here we go.

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The classifier is built.

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Okay so that's it.

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For this tutorial we fitted logistic
regression to the training set.

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And in the next tutorial

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we will be predicting the test results
using this classifier that we just built.

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All right
thank you for watching this tutorial.

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I look forward
to seeing you in the next one.

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And until then, enjoy machine learning.