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So in this video, we will learn how to do bagging technique.

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We expect that after creating.

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A model based on bagging the best performance of our model would be better than the broomed or the paltry.

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And are bagging can be done using the addendum for this back in only.

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And are bagging is like a special case of random forest in bagging.

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We'll be using all the variables, only the dataset will be bootstrapped and multiple training sets

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will be created.

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In random forest, there may be fewer variables, but the data set will again be bootstrapped.

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So since bootstrapping is being done, there is just a difference of number of variables to be considered.

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If I mentioned that I want all the variables to be considered, it will be considered as bagging.

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If I say that I want only a few of the variables to be considered, it will be random forest.

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So we will use random forest package only to do wood bagging and random forest.

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So the first thing is we have to install this package, that dumb forest.

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I'll go and underscore my.

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No, that the F of forest is capital.

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So random forces started.

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You can scroll and take that there is random forest.

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You either take it hit or run the Liberty Command.

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And this random forest is a No.

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Next to me, Set-aside.

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As I told you earlier, this adjusts for reproducibility of the desert.

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So if you set the seed to zero, you and I will get these same desert, said the seed.

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So now to preen our model.

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We will use this random for this function, bagging is the name of the variable which will get the information

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of the bag model to bagging, get information from random forest where the first parameter is formula.

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Formula is the same as earlier.

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We have collection as the variable to be predicted.

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After that, the lesson we need to give, the variables that are going to be used for prediction are

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the predictor variables.

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Since we are going to use all the variables.

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Therefore, I have put a dot after literally.

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Next, better my dad is data, these are trained data set.

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The last parameter is M.

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Right.

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Emigrate means how many of the predicted variables that we want to consider while building our model,

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if we use all the predictors to create all the models.

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This will be bagging sets in bagging.

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We have all the predictors, only different training dataset.

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If we reduce the number of M3, it becomes a case of random forest.

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So MPRI for us is going to be 17 because entrain D2C.

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If you look in the right, the strain dataset has 18 variables.

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One of them is the dependent variable.

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So therefore, we have 17 independent predictive variables.

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So Embry's equal to 70.

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I'll run this combine.

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Now, there is a variable called bagging created bagging contains information of the bag model.

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Now, using that model, we can predict the values on our best, it so will create new column in dataset

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called S. Dollar Bagging.

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So, again, to predict devalues using this model, you used to predict function.

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S dollar bagging.

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Very well, we'll get these values from predict function.

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The first barometer is the modelling, which is Baghi, and the second parameter is the deficit.

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So you predict bagging my test?

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I learned this discover.

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And now predicted values using the bagging model are saved in test dollar bagging with.

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Now, to find out the masc of these predicted bagging values will use this formula.

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Just masc bagging get value from the difference between predicted and actual values squared.

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And then we find the meaning of these values.

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So we'll run this command to get the MCO bagging.

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Now you can see that masc bagging has value here, which is nearly 52 million.

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You can compare this value with the other embassies.

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This MSE was the value of a tree that we created with a depth of three levels.

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It had a massive value of one hundred thirteen million when we created the full tree, that is.

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There was no restriction on the number of levels.

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Then we had an embassy value of ninety nine million.

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When we prune the tree, we got some improvement and we had a massive rally of 97 million.

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But when we are using bagging.

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The MSE value as it reduced significantly.

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And now the embassy is nearly 52, 53 million.

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So you can see clearly there is huge improvement in prediction accuracy when we do Bagi.

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The downside of using begging is.

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Like earlier, you were able to plot the decision tree very easily.

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That is not possible when you have done bagging.

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So by trading off, interpretively, that prediction accuracy, you are able to get better prediction

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accuracy.