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In this video, we are going to look at the code to build a regression tree.

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And we will build a regression tree on our movie data set.

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And using that tree, we will predict the values of boxoffice collection for our test set.

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The first step that we need to take to create this tree is we need to install some packages.

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To create this entry, we will be using our part package.

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And once a tree is created, we will use the R part or plot package to plot that decision tree.

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So we need two packages, our part and our part not.

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Again, if these are already in start, you do not need to install them.

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But if they are not, you need to run these two lines to install the other part and are part of the

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package.

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So I will run these two commands.

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And Condoling did.

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Now both the packages are installed.

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But if you go to the packages section, the take will not be dead.

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You can see we have these two packages.

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These are installed, but these are not active to make them active.

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We either in library are part and library are blocked or we just take it here.

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So we'll run these two commands to make them active.

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Now we can use these two libraries, training a regression tree is very simple, not just one line of

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code.

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This is that line we will use.

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The odd part funkin Gregory is the name of Abel in which we will get all the information or designation

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tree.

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And lately, I'm getting information from the, ah, part function.

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That formula is.

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Collection delayed dart, the meaning of collection delayed orders.

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Anything on the left of L.A., this very symbol?

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Anything to the left of this baby symbol is the dependent variable.

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So my dependent variable is collection.

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And I want to identify the relationship of collection with all the other variables in my data to represent

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all other variables.

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I have to write a dot.

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If I want to use particular variable, I can write the name of that particular variable also here,

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if I want to use two variables, I can write the name of both the variables and use a plus symbol in

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between them.

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So if I want to find the relationship of collection with marketing expense and production expense,

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I'll just write collection delay marketing expense plus production expense.

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So formula will tell the output function.

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Which is the dependent variable and which are the independent variables here?

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Collection is the dependent variable and DART signifies that all other variables except collection are

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the independent or predictable variables.

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The next parameter is data.

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Name of data is green.

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Because we want to use only retrains it to bring that model.

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The third parameter we are going to use is control.

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This barometer will help us control the length of the decision three.

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We do not want a very long decision tree, a small tree is more interpretable.

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And it has less chance of overfitting.

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So here, I'm just going to keep our depth of three.

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That is, it will have at Max.

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Three more layers from the initial load, which has the total population.

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If you want to know more about the are part function, that is what all other arguments can you use

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with our part?

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You just go to our bar and press EF1.

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It will open the help of our part function in the dating scene.

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You can see all the different argument that can be given.

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The arguments that we are giving are mostly mandatory.

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This control argument is not mandatory.

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It has some default values but to control the growth of this country.

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We have put this control argument to.

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So I will run this, come on, no.

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And you can see a new very well, Greg Tree is created and it has the information of the decision tree

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model on this trading site.

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Not if you want to look at this decision tree created by this single line of code.

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You can run this second line.

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The second line uses the passport law function of our partner plourde package.

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The first argument, and this is the variable which contains the disagreeably information for us, it

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was a.

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The second argument is not mandatory.

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It is just the color palette that we are going to use to lord this decision tree.

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You can use a different color palette.

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You can miss this argument also.

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If you do not write this, it will use some default colors of its own.

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The third argument is digit digit tells how many significant digits.

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Should that decision tree help?

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Once we applaud this decision tree, I will change the value of this digit to show you how the value

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in the decision tree changes.

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So let us run this command and Blätter diffidently.

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You can see on the right we have this decision tree plot.

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We can click on the zoom button.

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And it is the zoomed version of this decision tree.

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Let us look at this decision tree to see what this is telling.

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So the top node that is the initial node had all the observations.

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That is hundred percent of the observations.

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And the average collection of all the movies was nearly 45000.

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The first split is using the variable budget.

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At nearly thirty eight thousand, well, you saw the left side.

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As for movies which have budget less than 38000 and the right side is for movies which have budget more

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than to you told them.

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In the right side, you can see this node contains nearly 16 percent of the observations.

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And whereas this left node contains nearly 84 percent of the obligations.

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In these 16 percent of the movies, which are budget, more than 38000.

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The average collection is nearly seven retooled holen, whereas.

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In movies with less than 38000 budget, the average collection is nearly 39000.

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So there is a huge difference between movies which have budget more than 38000.

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And budget less than 38000.

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You can continue further to see the effect of different variables.

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At each node.

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So in this node, the split is on the Berek everything.

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And this node displayed is on trailered views and so on, since we mentioned that we need a maximum

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depth of three layers.

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This is layer zero.

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This is layer one.

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This is layer, too.

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And this is layer three.

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So this tree has a maximum depth of three.

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If we increase that control barometer of maximum debt to five, we will have a bigger three.

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Although that tree will be difficult to interpret.

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So here you can clearly see that the most important variable is budget after budget.

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It is nearly three levels.

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And then we have that iterating our marketing expense.

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Now, let us go back and see the effect of mentioning those digits.

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But we did.

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So if I put digits as Zito and done, come on.

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You can see that now I have.

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Now, I have this plot in scientific format that is, it is giving me values in 45 in two tenders to

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develop three format.

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So since this becomes a little bit difficult to read and understand, that is why I had Bridget is equal

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to minus three.

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That is.

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I want those three significant figures also, which were going into the.

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So if you wanted to be in the power, you increase the value of digital.

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And if you want more significant deserves to be displayed like this, you reduce the value of digit.

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The love we have created, we have trained our regression tree on the training set and we have used

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the Arpad outlawed function to block the integration de.

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You can use these regression trees in your presentations and in your document.

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And these can help you make important business decisions.

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No other part of using the C entry is to be able to predict the values for other movies or for future

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observations that we are going to get.

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Do predictive value, we use predict function.

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I'm going to add a new column in the test dataset.

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This column will contain the predicted values.

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Using the decision tree that we have created.

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So to find out the predicted values, we use this predict function in this predict function.

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The first argument is that variable, which contains the information of DDC entry, which was a three

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for us.

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The second argument is the data on which you want to predict.

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So that data is contained in the test, does it?

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And the third parameter is telling the type of response that you want to predict.

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Since we want continuous menus, we'll use type is equal to vector.

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If this was a classification decision tree, that is, we wanted to predict the classes, then we would

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have used a different type.

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That is Tabor's equal duck class.

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Here is a disintegration tree.

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We want a political director.

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So I will run this and we can go and look at the test dataset.

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Notice click on it.

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And in the end, you can see this column, the collection column has the actual values.

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And this last column, the red column, has the predicted values for are.

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We'll read it as it.

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So using the information in a tree, it has predicted these values for the desert.

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Now, one measure of performance of our model is calculating the mean squared error.

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And asked Orlandi, two related means squared error is the meaning of squared of differences between

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actual and predicted.

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So basically we find out the difference between predicted and actual V squared is different.

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And then we find, I mean, of all those values.

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If I run this, a new variable, MASC squared is created.

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It has this value.

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Which is nearly one hundred thirteen million.

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And this is the mean square difference between actual and deeply rooted values.

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Now, when we study the advanced techniques in the future, we calculate masc values for all those techniques

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and we will see that whether using those techniques reduces this MASC.

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So basically, the accuracy will be dulled using MASC if masc value is low.

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The model is more accurate.

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So this is the code to create our D.C. entry.

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Then to plot a D.C. entry and then to use that this entry to predictive values for another dataset.