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Hello, my friends.

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All right, let's
see if you got this right.

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Again, predicting the test. Results.

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And displaying the vector.
Of the. Predictions next.

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To the vector of the real. Results,
meaning the real.

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Purchase. Decisions.

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And I really want you to.

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Juggle with all your toolkits, you know,

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whether it is the data
preprocessing toolkit or your other.

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Machine learning models,
where you have indeed.

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Several tools inside.

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Because indeed, now.

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The tool we would like to use
is that. You know.

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A little piece of code

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that allows to display
the two vectors of predicted results.

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And real. Results.

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So what I expected you to do,
you know, to.

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Be the most efficient as you can.

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Was to go to part two regression.

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And then into the multiple linear
regression folder and then open this

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multiple linear regression implementation,
which indeed contains inside.

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So I already opened it.

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That tool
allowing to display the two vectors

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of predicted results and real results.

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I'm talking,
of course, about this tool. Right.

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We implemented it many times.

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That's why I didn't want to do it again
here in R.

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Just a. Regression implementation.

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Plus I.

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Really want to train you and incentivize.

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You to.

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Be the most efficient as you can

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by juggling between your different models
implementations.

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And so.

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If you did this, if you had the reflex
to grab that tool right here.

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In multiple linear regression
or many other models

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where we implemented that,
well congratulations.

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You did amazing.

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All right.
So here we're going to take this.

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And of.

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Course if you.

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Reimplemented it. Yourself
that's also. Amazing.

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Especially if you managed
to be more efficient than how we were.

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Able to be.

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All right.

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Because what we only have to do here
is to copy this little piece of code.

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You know that tool. And.

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Paste it.

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In a new.

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Code cell here
to predict the test results.

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Because indeed we have. All the.

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Same names here. For the vector of.

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Predictions y.

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Pred, which will be the result of the.

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Predict method.

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Applied to test.

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Set and called of course from our not.

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Regressor object but classifier object.

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There we go.

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So that's the.
First change you had to make.

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And then. Well. Since this time, you know.

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Our predicted purchase.

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Decisions
and the real purchase decisions are.

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Either zero. One.

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Well we don't need to add anything
here. To.

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You know, forced a number of decimals
after the comma to.

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Be only two.

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Right here
we're. Only dealing with integers.

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So we can remove this.

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We don't need this.

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And then final question here.

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Do we have. To change anything.

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Well absolutely. Not.

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And that's what I mean by,
you know, grabbing a tool and applying it

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on your new model
by only having to change 1 or 2 things.

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Here we only changed the name of the model
type, you know, from.

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Regressor to classifier.

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Okay. So let's check it out.

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Let's see if it works.

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Let's press play here.

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And indeed.

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We get the two vector is. Next.

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To each other with.

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First on the left.

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Your vector of. Predictions.

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You know of the.
Predicted purchase. Decisions for.

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All the.

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Customers of of. Course
the test set right.

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This was applied to excess here.

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So that's all the customers
of the test set.

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And on the right in the second column you.

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Have the real purchased decisions.

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And so here
what's interesting to see is to compare.

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The predicted purchase.

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Decisions to the real ones.

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For all. The customers in the test. Set.

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All right.

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So let's see for the first.

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Customer of the test set,

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you know remember of age 30
and estimated salary 87,000.

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Dollars.

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Well the prediction is no.

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This customer didn't buy the new SUV.

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And the real result is indeed no.

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In reality,
that. Customer didn't buy the new.

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SUV. Good. Same for the second customer.

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That customer was predicted

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not to buy that new SUV, and indeed
it did not buy the new SUV.

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Third customer this time
the third customer actually.

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Bought that new SUV.

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And our model predicted
that indeed, this new customer bought it.

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Well, it's funny, we actually have a lot
of a correct predictions.

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That's amazing. Right?

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All this so far is correct.
This is correct.

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And here.

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We go. We have our first.
Incorrect prediction.

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Here. Our logistic. Regression model.

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Predicted that this particular.

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Customer didn't buy the.

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SUV because we have a prediction
of zero here.

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But in reality that.

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Customer bought that
new amazing. SUV. Okay.

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Because the real result here is a one.

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Then here. Is correct. Correct.
And here we go.

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Another incorrect prediction

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where our model predicted again
that this customer didn't buy the.

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SUV, whereas in reality.

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That customer. But the new SUV.

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All right. And you see so.

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That looks really really good
actually we will get a.

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Very nice confusion matrix.

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I will explain very soon what it is.

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And mostly a.

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Very good accuracy because the accuracy.

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In the test set.

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Of course.

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Is simply
the number of correct predictions.

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Divided by the.

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Total number of observations
in the test set.

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And this. Is exactly
what we're about to get.

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In the test set.

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We will not only get the confusion
matrix showing.

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So there. You go.
I'm about to explain what it is.

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The confusion
matrix. Will show us exactly.

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The number of correct predictions
and the number of incorrect predictions.

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For the two cases
where the real result was zero.

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Or one to.

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Basically, we will have a nice matrix

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showing how many mistakes
and correct predictions our model made.

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And of course inside the same new.

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Step or code cell.

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We will compute.

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The accuracy and we will see what is the.

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Percentage of correct predictions
are moral made on the test. Set.

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So should I ask you to try to do it.

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On your own? Well, yes. Why not? Because,
you know, you.

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Just have to go to the API of scikit.

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Learn and. Figure out.

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How to make that confusion matrix
and how to compute the.

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Accuracy.
And now I'll just give you a little hint.

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You will.

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Have to look.

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Into the metrics module from scikit learn.