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Hello my friends.

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I hope you're feeling amazing,

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and I hope mostly that you tried
predicting that new result yourself.

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And if you did, I'm sure you succeeded
and if not, that's okay,

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because we're now
about to succeed together.

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All right, so let's do this.

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Let's create a new coattail here
to make that prediction.

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And so now, you know, with what we did
previously with the SVR, it's

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going to seem so simple
because indeed, in order

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to make that prediction,
since there was no feature scaling

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needed for decision tree regression, well,
we just have to take our regressor

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from which
we just need to call the predict method.

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And then inside where do we have to input.

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Well no transform method of course
because there was no feature scaling,

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but only that observation 6.5

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for which we would like to have
the corresponding salary.

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And then remember how we need to format
that single position level of 6.5.

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We have to put it,
of course, in a 2D array by adding here

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a double pair of square brackets

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inside,
which will put your observation 6.5.

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All right.

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As simple as that.

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And now well the exercise is done.

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We are ready to get this prediction,
this predicted salary of the position

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level 6.5.

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So let's see.

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Let's see what our decision tree
regression model returns

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compared to not only the previous
predictions of our other regression

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models, but also,
you know, compared to what this person

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mentioned earn in this previous company.

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All right.

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So let's execute the cell
and the predicted salary of the decision

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tree regression model
for our specific case study

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is $150,000.

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So before we make any comment on this,
because, you know,

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if we really stick

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to the scenario of our case study, well,
this prediction would actually be bad

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because it would be lower
than the requested

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salary of this employee,
and therefore that would not be good.

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But let's not comment on this, because
then you'll see, as I told you, that

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the visualization of the decision tree
regression results will not be pretty.

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And that's because, as I told you
at the beginning of this section,

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the decision tree regression
model is clearly not the best model

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to use on a single feature data
set, right?

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It is more adapted to data sets with many
features, a high dimensional data set.

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And that's totally fine,
because the code we are implementing here

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can be very easily used on other data
sets with other features.

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That's what I explained
in the first tutorial of this section

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with you know that only 1 or 2 changes
to make in the data preprocessing phase,

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but I still want to show you

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the visualization results,
because we can only do this in 2D.

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You know,
that's why I chose this simple data set.

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It's in order to show you at the end

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what the regression curve looks like
for each of the regression models.

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And so we're going to still visualize that
for the decision tree regression model.

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But you will see that
it will be clearly not well adapted.

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All right.

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So congratulation for that prediction.

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And now in the final tutorial
of this section we will plot the decision

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tree regression curve in high resolution
to see what it looks like.

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And until then, enjoy machine learning.