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Hello and welcome to this art tutorial.

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Here we are
at the final round of regression

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with our final regression
model random Forest Regression.

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In the previous section
we saw the decision tree regression model.

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So if now the decision tree regression
doesn't have any secret for you,

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then you will perfectly understand
random forest regression

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because random forest is just a
team of decision trees,

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each one making some prediction
of your dependent variable

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and the ultimate prediction of the random
forest itself is simply

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the average of the different predictions
of all the different trees in the forest.

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And actually,
at the end of the previous section

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about decision trees,
I asked you an enigma.

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The enigma was knowing the results we got
with one tree, what would be the result?

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With ten trees or 100 trees or 500 trees

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in terms of visualization
and in term of prediction.

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So I hope that after watching
the intuition tutorial made by Kirill,

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you actually asked yourself this question
and tried to predict

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what's going to happen here
with random forest regression.

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So let's find out about that.

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We are going to build a random forest
regression model and see what happens.

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So let's do it.

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We're going to start by selecting
the right folder as a working directory.

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So it's in part two regression.

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And here is the final regression model.

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We were building random forest regression.

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So let's go inside.

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And that's the right folder we want to set

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is working directory with the position
salary CSV file.

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So let's click on this more button
and set as working directory.

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All good.

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And now let's take our regression template
to build this model efficiently.

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So we're actually going to take everything
from here to the bottom.

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But we will only include this code section
to visualize the regression

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model results.

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Because you understood
that the decision tree regression

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model is a non continuous regression
model.

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And since random
forest is a combination of decision trees

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then it's a combination of non
continuous regression model.

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And intuitively we understand
we can guess that the random forest

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regression model
is not going to be continuous either.

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So since this code doesn't work for non
continuous regression model

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we will actually use this one.

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That works perfectly for it.

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So I'm going to copy this paste
that here and remove this section.

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That is non appropriate for non continuous
regression models.

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Here we go.

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And now the template is ready.

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Let's change the basics.

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Let's replace here
regression model by random forest

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regression.

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Visualizing the random forest
regression results

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and fitting random forest
regression to our dataset okay great.

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So now let's build the model
which is in this section here.

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So let's remove this.

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And as usual we're going to import
the right library for the job.

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And then use a function
to build our random forest regressor.

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So the package we are going to import
is called random forest.

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So for those of you
who don't have the package installed

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in your packages here,
well you can check it out.

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Mine is already installed
because I used it before,

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but I'm going to write this line here
for those of you who need to install it.

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So install dot packages,

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parenthesis and in quotes random.

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So no capital R but then capital F.

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Oh right. All right random forest.

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And so I'm not going to install it
because mine is already installed.

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So I'm going to put that in comment.

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But if you want to install it
you just need to select this line as

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I just did and press Command and Control
plus enter to execute it.

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And this will install the package
properly.

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But here I'm going to put in command
by pressing command plus shift plus C.

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Here we go.

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And now when we have to do is to add this
you know library

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random forest
to actually automatically select

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the box here to import
automatically the random forest package

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when we execute the whole code
or the section.

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So that's important.

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And now time to build the regressor.

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So let's do it.

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We're going to call regressor regressor
as usual to keep things simple and equals.

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And now the function that we're going
to use is also

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random forest written the same.

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And so now let's add some parenthesis.

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And now let's press
F1 to have a look at the arguments okay.

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So the arguments are here.

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And the first argument is data.

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But as you can see it specifies
that it's an optional dataframe.

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And we could use this argument
to build our regressor.

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But we're going to use the main arguments
to specify, you know, the independent

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variables on one side and the dependent
variable and another side.

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And to do this we're going to use these
two arguments x and y.

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So x will contain the matrix and features
that is the independent variables.

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And y will contain the dependent
variable vector that is the salary column.

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So let's first input these two arguments.

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So the first argument is x equals.

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And so we have several ways
to take our independent variables.

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So one of the way is to take our data
set here.

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And then choose the right columns
of the independent variables.

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And you know our data set is composed
of two columns.

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The first column indexed by one
which is the independent variable column.

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And the second column indexed by two
which is our dependent variable column.

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So here we need index one because
we want to take the independent variable.

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All right.

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Now next argument.

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The next argument is y
the dependent variable vector.

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And now as you can see
y is expected to be a response vector.

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It's actually a vector.

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And here it expected to have a data frame.

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So by using this one index into brackets
here I actually import a data frame.

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But here to get a vector
I actually need to use another trick,

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another technique
which is in know to use a dollar sign.

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And then the name of the column,
which is of course salary.

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And that will give me a vector.