In this video, we will learn how to create a possible Smardon and Biton.
So let's first import the ACCE boost as X DBE.
So if this Ximo says not installed into your system, you first have to install it to install it.
You have to write up, install extreme boost.
So go to your command prompt.
Use a keyboard shortcut over no art and write CMB and head.
Okay.
And here you can write that.
Doug.
Thanks to boost.
Now, we have installed store loaded cibils.
And if you are using Anaconda.
You can also use an opened up prompt and sort of tip in so you can write KONDA this X reboost.
That will also work.
Let's again import exhibitionist.
So we have important egg symbols and to look at the documentation of symbols.
You can click on this link.
Here you can get all the information you won for excuse.
Let's look at the parameters.
So there are multiple parameters on exit boost.
We can divide this parameters in two, three categories vs. the general parameter, such as learning
rate, learning where it is known as ETR, etc..
And then there are three related parameters, such as maximum BEB Gamma Gamma is similar to your minimum
sample size.
Then subsample Dror.
And then there are learning task parameters.
Oh.
If you remember, the only difference between and will send extra boost is in the regularisation part.
And if you are aware of anyone, then a two regularisation techniques which are laso and reach in case
of linear regression.
There we have shrinkage parameters for the number of variables we use in our model.
So those parameters are Alpha and Lemina.
So there are many people that I went to to see it.
Go through documentation to look at all the parameters that you want to apply.
Will only covering a subset of those parameters which are important.
Let's first create exhibits classifier.
We are using maximum depth of five.
And number of trees as 10000.
And learning rate of point three.
Let's run this.
And let's for our data.
So our model is ready.
Let's get decorously score.
The accuracy is scored.
We are getting a success expunged six six percent.
So, as you can see, we are getting the highest score using Ximo Smardon.
And usually exhibits gifts, the best result out of random forest GBM or any other classified matter.
The Zimbalist, we also get another matter that this plot underscore the importance.
This my card will give us the graph of the relative importance of variables that we are using for our
modern food on this.
You can see we have our variables listed on the left hand side.
And we have the feature importance on this axis.
So as you can see, the most important feature from our variables is time taker.
The second most important feature is Twitter hashtag, then Greller views and so on.
And the least important feature is Johna Underscore Community.
This is another advantage of agcy boost first.
The prediction accuracy is very high.
And you also get the feature importance graph using X equals.
Now, as we discussed earlier, we can use grid search to provide multiple values of our hyper parameters.
So let's use grid search to optimize our exhibits classifier.
So first, I will create just a classifier with Tyber parameters that I don't want to change.
So I will write exhibit classifier with parameters and as symmetries, let's change it to 250 instead
of 500 so that we can quickly drain this model with learning rate zero point one and our numbers set
off 42.
Now we want to change all of this parameters.
First one is the maximum depth.
This the depth of our tree.
And we want to give values from three to 10 with interval of two.
So we want to give three, five, seven and nine.
We can do this either by providing a list or we can also broyd this in the form of fringe method.
So arrange range starting point and point and the step.
So if you are not comfortable with trends, you can also write three Colma, five comma, seven common
name.
Then we have the gala value.
This is almost same as the minimum sample split.
You can read documentation for more detail.
But this is almost same as minimum simple estate.
We are creating value of zero point one zero point one zero one three.
Now we have another parameter, which is subsample.
So if you remember in begging for each tree, we use a subsample of our data.
So we are seeing that old values of 80 percent and 90 percent for subsample value.
That means for each tree use 80 percent of their data.
This 80 percent is random data.
So each time we will select a portion of our data.
Now the next parameter is Colomb subsample by tree.
So this time and sort of data use 80 percent of feature to create ERP.
So randomly we selected two percent of feature.
And we will create a random tree.
Then we have Greg ULFA.
This is the regularization parameter.
And we are providing three values, zero point zero one zero point one and one.
So we are training our model on all these values of fiber parameter.
And after training or model, we will select the best model out of these models.
So let's run this again.
You can notice that we have created all this hyper parameters in the form of dictionary with our hyper
parameter to name as Ghys.
And the range of hyper, I mean, as values.
And again, in grid search, we first have to mention our classifier, which is exhibit underscores
VLF.
Then we have to.
Mentioned the parameters.
We want to pass in the form of dictionary.
We are using padam underscore test one and then cross-validation.
We want cross-validation will to be five.
And we want the scoring to be of accuracy.
Let's run this.
And now we are fitting our Ekstrand in train down to this model.
It will take some time.
And remember that we are using all our models as a classifier models.
All this are available for regression also.
So if you have a continuous very even, you can use that regression.
What is one of these techniques?
Sylvia fitted out grid search.
So let's.
Look at the pet.
I mean.
So for best, Märta.
So we are getting our best mardon when we are using.
The column simple as Boin date, the garment value of point three, maximum depth of seven.
Regularisation as far as zero point one and subsample of zero point eight.
We'll save this more than that is the best modern and do another witty one.
That is Siwiec Zeehan Discourse VLF.
Then we can find out that curacy.
And there scums, so doobies for physics, but say.
Since our data is less and we have used less number of Greece, we are getting our degrees in accuracy
score.
But if you have a large amount of data and if you select large number of Greece, you will definitely
get higher accuracy as compared to a single random XY Wall Street, because here we are picking a best
model out of all this morten's.
That's all for Sewall's.
Thank you.