And this will do will create our first model using our extreme and wide-screen data.
Will follow the same step as we do for every classification or regression model in Ashkelon.
That is will first create our regulation or classifier object.
Then we'll fit a word Ekstrand and Vytorin data.
And to that classifier or regress that object.
And then we will predict the word dependent variable values using our model and extreme and X test data.
First, we need to import CCRI from Escalon.
And then will create an object rectory here, rectory is our variable name with a sense for regression
tree.
And we lose Creedmore decision tree regrets at.
Greed.
Don't be angry, Degreaser is our function.
And there are several parameters inside it.
If you remember, we have discussed the terminologies of different dumps of Kree.
You will find all those items as a parameter here.
And if you remember what his maximum depth that is.
Number of layers in your tree here.
We don't want to overfit our tree.
That's why we are keeping maximum depth equal to three.
You can keep any other values.
But to make your tree interpretable, don't accept this maximum depth.
Values beyond five.
We are running our first murder with maximum depth equal to three.
You can choose any other values if you want.
We will execute this.
That green object is ready.
Now our second step is Stopford extreme.
And by train and to our direct gry object.
We'll just use Rectory don't fit, don't fit, it is our method.
And there are two parameters here, extreme and wide-screen.
So what?
Right.
CCRI regulator object is ready.
We can now use this as a treat to predict values of why using our x crane and expressed very well.
No, predicting values is very easy.
Will use victory.
Don't predict method.
And in this my third, we just have to provide our expertise wills, as you can see, we are saving
our credit values, losing.
Why underscore green, underscore pride and why underscore this?
Underscore pride for our extranet and X has taught us it will just execute this.
Come on.
Now, we have created this Vikrant bread and wide spread, we will use this particular to a.
To calculate the performance of our model.
Now to see the projected values.
You just have to, right?
Why underscore tests, underscore pride.
This will give you the trade we'll use on our test dataset.
If you execute this are authentic values.
Now, in the next lecture, we will use this predator to allow lose to calculate the performance of
our modern.