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Hello my friends.

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All right.

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So how did you do with the little exercise
here?

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Predicting the test set results.

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So, based on what
we've just done in the previous tutorial,

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you know, when training the simple linear
regression model on the training set,

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and also based on the hint
I gave you, you know, this predict method?

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Well,
you should have been able to crack this.

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All right.

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So now we're going to implement

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the solution together
starting by creating this new code cell.

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All right.

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So first let's just
explain what we want to do here.

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We want to predict the result
of the observations in the test set.

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And so let me show you once again
the data set.

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So that's the whole data set
containing the 30 observations.

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And we actually splitted this data set
into the training set and the test set.

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The test size was chosen to be 0.2,
meaning that

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20% of all these observations
went into the test set.

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So 20% of 30 is actually six.

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So let's say
just to simplify the explanation

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that the observations of the test set
are the last six ones.

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You know, 123456.

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So let's say all of these observations
went into the test set.

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Well, what we want to do now is predict
for each of these observations,

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meaning for each of these employees
the salary.

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Right.

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So what we're going to input in
that predict method is exactly

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the number of years of experience
for each of these six employees.

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And our model will predict the salaries.

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So the salaries that we see here,
you know, the last six ones

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are the exact salaries.
You know the truth.

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We call it the ground truth.

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And when calling the predict method
on these

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six numbers of years of experience,
we will get six predicted salaries.

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And so what?

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We'll want to do
then will be to compare the predicted

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salaries to these real six salaries okay.

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And we will do that
in the last steps of this implementation.

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When visualizing not only the test results
but also the training

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set results you're going to see,
everything will be super clear.

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All right so let's do this.

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Let's get the test results by.

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First.

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You know, in order to call a method
we first need to call the object itself

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which is regressor.

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So that was the first step
you had to do here.

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Then from our object we add a dot here.

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And we call the function we want.

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Oh wow. It's funny.

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The Google collab
actually guessed what I was about to call.

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That's I never noticed that.
But anyway, yeah, that's perfect.

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We want to use the predict method.

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Of course that was the hint.

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And this predict method, you know
as any function expects some arguments.

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And so now according to you what does
we have to input in this predict method.

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Well that's simple.

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Once again
you know we want to enter the features

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meaning numbers of years of experience
and not the salaries.

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That was just for the training set.

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Here we only need
the numbers of experience,

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because from the numbers of experience
we want to predict, the salaries

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and the numbers of experience
are exactly contained in X test.

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Right.

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Because we want the numbers
of experience of the test set.

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And so the only thing we had to input
here is X test.

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And there you go. That was the solution.

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Okay.
So as you can see once again very easy.

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That's the beauty of the libraries.

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You can just do anything you want
in usually one or 2 or 3 lines of code.

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And here we just had to call the predict
method to make some predictions

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from a model that is of course
already trained on the training set.

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All right.

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Now, because we were going to visualize
then the training set results

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in a test result.

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I'm actually going to put

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all these predictions because this returns
actually a vector of predictions.

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You know, a vector

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containing the predicted salaries
of the employees in the test set.

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So I would like to put all these predicted
salaries in a vector,

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therefore in a new variable.

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And therefore here I'm creating this
new variable which I'm calling y pred.

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You know, as opposed to y test
which contains the real salary.

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So make sure to understand
why test here contains the real salaries

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and why it here contains
the predicted salaries.

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And now in the next steps
we're going to compare white bread to

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white test and also will compare
Y train to the predicted salaries

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in the training set.

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And the last two steps
of this implementation

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visualizing the training set results
and visualizing the test results.

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We will get amazing graphs, super clear
super simple to understand,

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and we will clearly see how well our model
was trained with this visualization,

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and how well our model was able

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to predict new observations
with this visualization.

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So let's do this in the next tutorial.

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And until then, enjoy machine learning.