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So now let us see how to create a random forest model in our.

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Again, to create random forest model, we need to use the package.

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Random forest.

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This is the same package we use to do bagging and Jawdat.

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This package is installed and it is active.

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So since this package started for me, I'll just run this Libatique a one to make sure that it is active

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for me.

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Next step is to use the land random forest function to create the model random for is the name of my

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variable, which will contain the information from this random forest function.

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The first barometer is the formula.

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This is the same collection as my dependent variable, all of that on my independent ready world data

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is brain data.

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And three is the barometer, which specifies the maximum number of threes that this model should create.

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If you remember in bagging, we used a barometer called M3 and we specified that Emtriva value should

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be equal to all the other independent variables.

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So we equated it to 17.

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In this scenario, since Random Forest considers less number of rebuild, that is a subset of the total

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predicted space.

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Therefore, either we specify Emtriva less than 17.

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So maybe we can say M3 is equal to five or embrace equal to six, something like that, or we do not

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specify anything.

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And are will use a default value.

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So by default, since this is a regression model, I will use PMA three.

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Since we have 17.

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Predictor variables.

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B by three will come out to be five point six six.

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Which is nearly six.

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So probably ordering take em crazy equal to six.

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If you want to use other value, you can just write a comma and the best way M3 is equal to some of

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that value which you want to use.

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So I love this model.

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And random forward variable has the information of the random forest model.

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Now, using the predicate function I predicted devalues and using this formula for MASC.

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I calculated the MSE for Random Forest.

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Now you can see that a messy for random forest is coming out to be 41 million, nearly four bagging,

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it was 54, 55 million.

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So you can clearly see an improvement in the model.

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There is nearly a 20 percent improvement in terms of masc of the predicted values.

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So just by changing the value of M3 in this whole process, that is instead of using Embry's equal to

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17.

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We are using embryos equal to nearly six.

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I am getting a huge improvement in prediction, accuracy of my model.

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And by the way, I hope since I'm doing bagging and rainforest's only for recreation, I hope that you're

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doing it for classification on your own.

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You know how to run that classification model.

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Only thing you need to add is another parameter here, which is method.

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So that works for bagging and random forest as well.

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So my suggestion is that you should go ahead and do this for classification also.

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And see the improvement in classification prediction accuracy.

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So do this work classification, make their confusion metrics and see the improvement in prediction,

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accuracy, using, bagging and random forest.