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And this will do will create our first model using our extreme and wide-screen data.

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Will follow the same step as we do for every classification or regression model in Ashkelon.

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That is will first create our regulation or classifier object.

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Then we'll fit a word Ekstrand and Vytorin data.

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And to that classifier or regress that object.

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And then we will predict the word dependent variable values using our model and extreme and X test data.

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First, we need to import CCRI from Escalon.

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And then will create an object rectory here, rectory is our variable name with a sense for regression

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tree.

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And we lose Creedmore decision tree regrets at.

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Greed.

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Don't be angry, Degreaser is our function.

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And there are several parameters inside it.

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If you remember, we have discussed the terminologies of different dumps of Kree.

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You will find all those items as a parameter here.

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And if you remember what his maximum depth that is.

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Number of layers in your tree here.

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We don't want to overfit our tree.

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That's why we are keeping maximum depth equal to three.

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You can keep any other values.

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But to make your tree interpretable, don't accept this maximum depth.

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Values beyond five.

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We are running our first murder with maximum depth equal to three.

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You can choose any other values if you want.

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We will execute this.

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That green object is ready.

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Now our second step is Stopford extreme.

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And by train and to our direct gry object.

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We'll just use Rectory don't fit, don't fit, it is our method.

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And there are two parameters here, extreme and wide-screen.

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So what?

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Right.

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CCRI regulator object is ready.

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We can now use this as a treat to predict values of why using our x crane and expressed very well.

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No, predicting values is very easy.

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Will use victory.

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Don't predict method.

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And in this my third, we just have to provide our expertise wills, as you can see, we are saving

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our credit values, losing.

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Why underscore green, underscore pride and why underscore this?

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Underscore pride for our extranet and X has taught us it will just execute this.

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Come on.

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Now, we have created this Vikrant bread and wide spread, we will use this particular to a.

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To calculate the performance of our model.

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Now to see the projected values.

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You just have to, right?

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Why underscore tests, underscore pride.

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This will give you the trade we'll use on our test dataset.

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If you execute this are authentic values.

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Now, in the next lecture, we will use this predator to allow lose to calculate the performance of

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our modern.