Now, the next model we want to create is a double smarted.
So the steps are almost seem reinforced import at Abus classifier.
And then we can provide hyper parameters and I was classifier and created our redoubles classifier object.
We will use our Ekstrand in y train data.
To train this model and then we'll find no accuracy score on our test data.
Let's look at the documentation of that most classified.
Here, our first parameter is base estimate.
Unlike other techniques here, you can provide your base estimate there as well.
By default, that's the decision.
Greek classifier of maximum deafen.
But you can also provide random forest or any other classified as a base, a scimitar for doubles and
doubles will perform, boosting technique on that classifier.
Now, the next parameter here is an estimated.
Again, this is the number of trees we want.
Then we have learning rate this, the learning rate for our Addabbo Smardon.
We have also to discuss, in a word, to re lecture.
So for our first mardon, we will use a learning rate of zero point zero two and the number of crease
to be five tosing.
Let's sit on this, Martin.
We have created a world object.
We will photoed explain why drin due to.
No more delays already.
So by default, we have created this model on their decision, Tree of Depth one.
You can see the by default value of our base basis, someone that is mission creep classifier maximum
depth.
So we have frittered our air, the boost on that classifier, and we can calculate the accuracy score
by just running accuracy score and using the predicate function.
So the accuracy here is 62 percent.
No.
Let's create another classifier object.
Here we are using a word random forest classifier.
We have already created this classifier about.
This is Overclassify.
We are using this classifier with our doubles classifier here.
I have reduced the number of Greece to 500.
And I will also change.
Learning that put zero point five since I have.
Decreased my number of grease.
Let's run this.
Green, there were more done on this new object.
Now, let's get the accuracy score for this morning.
This way again, the new accuracy score is 64 percent.
So you can see that here we were using just this January.
Here we are using random forest as our base classified and that have increased the accuracy score from
62 percent to 64 percent.
Again, you can use grid search to optimize this learning rate and estimates, and you can also provide
different models as your basis simulator in the grid search.
So try a different combination and try to find the model which will give you accuracy scale more than
this is school.
Thank you.