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In this video, we will learn how to create a possible Smardon and Biton.

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So let's first import the ACCE boost as X DBE.

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So if this Ximo says not installed into your system, you first have to install it to install it.

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You have to write up, install extreme boost.

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So go to your command prompt.

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Use a keyboard shortcut over no art and write CMB and head.

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Okay.

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And here you can write that.

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Doug.

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Thanks to boost.

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Now, we have installed store loaded cibils.

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And if you are using Anaconda.

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You can also use an opened up prompt and sort of tip in so you can write KONDA this X reboost.

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That will also work.

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Let's again import exhibitionist.

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So we have important egg symbols and to look at the documentation of symbols.

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You can click on this link.

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Here you can get all the information you won for excuse.

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Let's look at the parameters.

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So there are multiple parameters on exit boost.

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We can divide this parameters in two, three categories vs. the general parameter, such as learning

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rate, learning where it is known as ETR, etc..

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And then there are three related parameters, such as maximum BEB Gamma Gamma is similar to your minimum

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sample size.

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Then subsample Dror.

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And then there are learning task parameters.

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Oh.

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If you remember, the only difference between and will send extra boost is in the regularisation part.

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And if you are aware of anyone, then a two regularisation techniques which are laso and reach in case

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of linear regression.

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There we have shrinkage parameters for the number of variables we use in our model.

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So those parameters are Alpha and Lemina.

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So there are many people that I went to to see it.

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Go through documentation to look at all the parameters that you want to apply.

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Will only covering a subset of those parameters which are important.

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Let's first create exhibits classifier.

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We are using maximum depth of five.

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And number of trees as 10000.

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And learning rate of point three.

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Let's run this.

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And let's for our data.

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So our model is ready.

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Let's get decorously score.

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The accuracy is scored.

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We are getting a success expunged six six percent.

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So, as you can see, we are getting the highest score using Ximo Smardon.

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And usually exhibits gifts, the best result out of random forest GBM or any other classified matter.

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The Zimbalist, we also get another matter that this plot underscore the importance.

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This my card will give us the graph of the relative importance of variables that we are using for our

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modern food on this.

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You can see we have our variables listed on the left hand side.

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And we have the feature importance on this axis.

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So as you can see, the most important feature from our variables is time taker.

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The second most important feature is Twitter hashtag, then Greller views and so on.

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And the least important feature is Johna Underscore Community.

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This is another advantage of agcy boost first.

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The prediction accuracy is very high.

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And you also get the feature importance graph using X equals.

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Now, as we discussed earlier, we can use grid search to provide multiple values of our hyper parameters.

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So let's use grid search to optimize our exhibits classifier.

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So first, I will create just a classifier with Tyber parameters that I don't want to change.

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So I will write exhibit classifier with parameters and as symmetries, let's change it to 250 instead

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of 500 so that we can quickly drain this model with learning rate zero point one and our numbers set

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off 42.

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Now we want to change all of this parameters.

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First one is the maximum depth.

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This the depth of our tree.

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And we want to give values from three to 10 with interval of two.

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So we want to give three, five, seven and nine.

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We can do this either by providing a list or we can also broyd this in the form of fringe method.

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So arrange range starting point and point and the step.

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So if you are not comfortable with trends, you can also write three Colma, five comma, seven common

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name.

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Then we have the gala value.

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This is almost same as the minimum sample split.

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You can read documentation for more detail.

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But this is almost same as minimum simple estate.

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We are creating value of zero point one zero point one zero one three.

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Now we have another parameter, which is subsample.

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So if you remember in begging for each tree, we use a subsample of our data.

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So we are seeing that old values of 80 percent and 90 percent for subsample value.

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That means for each tree use 80 percent of their data.

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This 80 percent is random data.

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So each time we will select a portion of our data.

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Now the next parameter is Colomb subsample by tree.

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So this time and sort of data use 80 percent of feature to create ERP.

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So randomly we selected two percent of feature.

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And we will create a random tree.

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Then we have Greg ULFA.

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This is the regularization parameter.

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And we are providing three values, zero point zero one zero point one and one.

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So we are training our model on all these values of fiber parameter.

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And after training or model, we will select the best model out of these models.

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So let's run this again.

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You can notice that we have created all this hyper parameters in the form of dictionary with our hyper

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parameter to name as Ghys.

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And the range of hyper, I mean, as values.

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And again, in grid search, we first have to mention our classifier, which is exhibit underscores

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VLF.

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Then we have to.

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Mentioned the parameters.

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We want to pass in the form of dictionary.

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We are using padam underscore test one and then cross-validation.

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We want cross-validation will to be five.

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And we want the scoring to be of accuracy.

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Let's run this.

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And now we are fitting our Ekstrand in train down to this model.

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It will take some time.

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And remember that we are using all our models as a classifier models.

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All this are available for regression also.

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So if you have a continuous very even, you can use that regression.

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What is one of these techniques?

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Sylvia fitted out grid search.

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So let's.

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Look at the pet.

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I mean.

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So for best, Märta.

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So we are getting our best mardon when we are using.

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The column simple as Boin date, the garment value of point three, maximum depth of seven.

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Regularisation as far as zero point one and subsample of zero point eight.

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We'll save this more than that is the best modern and do another witty one.

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That is Siwiec Zeehan Discourse VLF.

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Then we can find out that curacy.

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And there scums, so doobies for physics, but say.

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Since our data is less and we have used less number of Greece, we are getting our degrees in accuracy

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score.

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But if you have a large amount of data and if you select large number of Greece, you will definitely

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get higher accuracy as compared to a single random XY Wall Street, because here we are picking a best

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model out of all this morten's.

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That's all for Sewall's.

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Thank you.