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Hello my friends, and welcome

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to this new practical activity
for Decision Tree Classification.

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And now please follow me into part
three classification.

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And to implement this time
the decision tree classification model.

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And as usual
we're going to start with Python.

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This Python folder contains two files.

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The implementation of the decision tree
classification model in ipynb format,

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and the same data
set social network at the CSV,

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which contains information
of 400 customers of a car dealership

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which just released
a brand new luxury SUV.

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And the strategic team
gathered this data to understand

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which customers buy the most SUV in order

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to target them with ads
that will be posted on social networks.

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So each of these rows correspond
to different customers

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and for each of these customers,

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we have two features
the age and the estimated salary with

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which we're going to predict the dependent
variable purchased, which takes

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binary values zero, meaning
that the customer didn't buy the SUV,

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and one meaning that the customer
but the SUV.

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All right, let's do this.

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Let's see if decision tree
classification can beat

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the previous accuracy
we got so far, meaning 93%.

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So without further ado, let's open
that with Google Colab.

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Choose your favorite.

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And I'm going to put it here because
this is our next classification model.

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All right. Perfect.

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So once again this implementation results
from that classification template

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we made in the first section of this part
three

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when we implemented
the logistic regression model.

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And therefore the only cell
that changes here in this implementation

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is, as usual, that cell where we build
and train the classification model.

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Right.

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This cell here and all the rest, you know,
all the other cells are exactly the same

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as in the logistic regression
code template

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or you know, the classification
code template,

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because indeed
we use the same variable names classifier.

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And then x train x test
everything is the same except the cell.

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So once again
we're going to re-implement this cell.

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But to do this we need to create a copy
because this is in read only mode.

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So right now
we're going to create that copy

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on which we will be able to re-implement
that cell.

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All right.

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So let's scroll down again

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to find that cell where we build and train
the decision tree classification model.

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Now let's put it in the trash immediately
because I don't want you to see,

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you know, the name of the class, because
indeed I want you to find it on your own.

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So there you go.

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Now is the time you press pause
on this video to find the right class

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name that allows us to build
the decision tree classification model.

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You will have to either look for it
directly through an online search

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or that other option is to look for it
inside the scikit learn API.

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And this is exactly
what we're going to do together.

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And all right let's do this.

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So let's go to the scikit
learn API to find the class

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that allows us to build a decision tree
classification model.

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All right.

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So API

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and by memory you know let's suppose
actually that I have no idea of the module

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or even the class that built this decision
tree classification model.

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So here I'm scrolling down, you know,
to observe the different modules.

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Maybe it is an ensemble method
because you know, I know that

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random forest we built, you know,
the Random Forest Regressor in part two.

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Random forest is an in simple method.

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But no, here
we don't see the decision tree.

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So let's scroll back down again.

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Which makes sense, right?

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Because the decision tree
is just a single model.

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It is not an ensemble of models.

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All right.
So many learning metrics. No, no.

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You know it's really important

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that you get familiar with this API
because the more you get familiar,

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the more expert you'll become
and the better you'll juggle

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with all the machine

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learning tools you know, besides the one
that I'm giving you in this course.

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All right,
so here I'm scrolling down more.

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All right.

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Preprocessing,
random projection, semi-supervised

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learning,
support vector machines, decision trees.

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There we go.

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You know, usually the module
you're looking for in the circular

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API will have the same name here
as the model you want to build.

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Right.

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And you know,
this is organized in alphabetical order.

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So that's why I wanted you to do this
exercise.

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It's actually very easy
to find what you want.

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All right decision trees.

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Now which one
do we want to get among these.

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Well that's of course the first one.

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You know, that was the one we used in part
two regression.

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But now we're doing classification.

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So we want the decision tree
classifier class.

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All right.

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So now what do we have to do first.

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Well let's do you know
what do we have to do

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anyway which is to import that class.

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And so I'm copying this.

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And then back in my copy
we're going to create a new code cell.

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We're going to paste that.

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And once again
we're going to start from scikit learn.

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And then the tree module by scikit learn.

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And from that tree module
we're going to import

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the decision tree classifier.

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So that's the first step right.

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You know it by heart. Now.

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And now the second step
I don't even have to tell you.

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It is
of course to create our classifier object,

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which will be created
as an instance of this decision

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tree classifier class
and which will represent nothing else.

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Then the decision tree classifier model.

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All right.
So here we add some parenthesis.

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And now the second question is what
do we have to input here as parameters.

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You know if this decision tree
classifier class.

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So let's see.

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Let's go back to the documentation
to scikit learn API.

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And well we will just change one
you know default

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value of the parameters
which is this one criterion.

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So the default value of criterion is Gini.

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But with respect to what you learned
in the theory with key rules

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intuition lecture, we're going to choose
an entropy criterion.

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So the criterion is of course the function
to measure the quality of a splits.

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And that quality is measures by entropy.