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In this video, we are going to learn how to create a steam boost model not.

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This is going to be a little bit different from the models that we have been creating.

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Usually we just had one test data and one train data in the form luvvie mentioned.

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What is the dependent?

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And what is the independent variable?

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And we told what data to be used and the model was trained.

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But for training a model using XY boost, we need to prepare the data in a particular format in that

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format is called R B Matrix.

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So we'll first learn how to get the data ready and that format in which it's Tibbles will be able to

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run on it and train the model.

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The package that we are going to use for this is Izzy Boost.

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So if it is installed, you can just stay out of it.

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Celebrity deliberate.

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Eagleman If it is not, we will install the package.

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The package it started will run Liberty Command.

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Now to prepare the data.

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First we have to separate the dependent and independent variables.

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And when we separate the dependent variable, that is the variable that we want to predict.

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That variable should be in the form of a Boolean added.

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It should contain values.

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True or false.

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So if you remember my start, take Oscar.

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Very well.

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Contained value.

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Zero or one.

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I will create a new variable, Greenway, and this Greenway will contain boolean values.

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It will contain true if static Oscar contained one.

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And it will contain false if static Oscar contained ZEW.

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So I'm checking here whether the values won or not.

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If it is won, then it will have value.

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True.

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If it is zero, then it will have value.

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False.

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So that.

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Does it underscore my.

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And look at this train very, very well, it it does very well and it has values falls through True-Blue

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and so on.

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You can by train my head and I need to see all the values of train my.

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Next for Green Eggs, we will.

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We need to create a model matrix.

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The point is that we cannot have any categorical variable containing categories.

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We need to convert categorical variables to dummy variables.

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That is the variable.

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Should that have value only 011?

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So if you have a categorical variable containing value, such as.

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Yes and no.

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For example, if we go and open the train dataset.

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And we look at the X 3D available variable.

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It has values.

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Yes, I know.

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And there is a genre variable which has values, drama, comedy, action and thriller.

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These labels are not allowed in next reboost.

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We need to convert these variables to numeric paper variables.

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So one way of doing that is to create a dummy variable for these variable.

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So a dummy variable of 3D available would have won if this value is.

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Yes, and it will have zero if this value is no.

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Similarly for Donald Variable, we will make N minus one.

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Dummy variables.

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That is.

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There are four categories.

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We will create four minus one.

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That is three dummy variables.

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The first time you variable will have value one wherever DeJohn already of evil has value.

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Drama.

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The second dummy variable of John, it will have value new one whenever the John Elway variable values

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comedy.

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The third will have value one whatever Dorna has value Trilla when all the three dummy variables are

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zero.

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John, it has a value of action.

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So in this way, we will create dummy variables for all the categorical variables.

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And a short way to do that is create a model matrix.

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So we will create a new tree next video when?

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It will have the values from model matrix function, which has this formula.

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Any variable on the left of this delay will not be converted to dummy variables.

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Variables on the right of this delay will be converted to the movie deals.

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So you want all the other categorical variables to be converted to a dummy variable.

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So there is a dark.

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Then we have a minus one.

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This is to delete the first column of the created dummy variables, the data to be.

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Well, the one is Brain C..

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Let's run this and we will look at three next so that you get a better understanding of what we have

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created.

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So this is the Kleenex.

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So if we look at this next one, evil.

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It has all these categorical variables converted to rebuild.

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You can see that extreme liberal has now dummy variables.

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John, it has three.

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We should delete this extra dummy variable, which can.

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Well, you know, and rest of it is ready.

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So we'll just go and delete that extra variable.

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To do that, we will write this green X gets green X

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comma, minus two.

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Will this minus 10 is because we want really deep gold column.

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So we'll run this combine.

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You can go to the train next day to confirm that the 12th column is gone.

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So with this hour, Train X data is ready in the model matrix format.

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Now, the same thing we need to do with the test.

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But also will separate the Y and X, part of it will run best Y is equal to this condition so that we

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have bestway indeed boolean format.

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That is, if star take Oscar in S to C dataset is one, it will contain value.

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True, if it is zero.

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It will contain false will run this test y is created with proof false values.

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Or test exile's.

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Also, we'll run the same thing.

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And we will believe these call column just to confirm they'll open their stakes scrawl and check if

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it is again available at World Vision.

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It does.

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So we will copy the skyline to the change train to test.

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I'm it.

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And Test X now has grown to everyone's.

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Now, as I told you, a extra boost takes input as a B matrix so two can create a D matrix.

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This is the code that we are put on SGV door.

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The matrix is the function.

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Data is the expert.

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The train X model that we created and label is the part that is the classification part that we want

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to predict.

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Similarly for test also, we will create this de matrix.

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So we'll run these two commands to create the data and do the B Matrix format.

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So by doing all this, we have prepared the data to be run into a SCV XY boost function.

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Now we will use this X matrix to train the model so we use SD boost function data is equal to X matrix.

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This is the data and drone is the number of iterations that the boasting algorithm will do for now.

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I have ordered 250.

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You can change it to 20 or 200 to see the performance objective.

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I have said to my date softmax.

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So let us see what are the objective options that we have.

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I have pressed one on extra boost to openly help on S to boost.

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So we'll put it best F1 to openly help what I see most in this.

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We want to look at the objective barometer.

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If you scroll down, you can see this is a task parameter.

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In this, we specify the learning task.

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If you're going to do linear regression, you use rig linear.

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If you want to do logistic regression, you write rig logistic.

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If you want the model to classify.

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You can use multi softmax, which is setting the XY boost to do multiclass classification using softmax

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objective.

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So sense are objective risk classification here we will use multi softmax if you have the objective

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of regression.

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You can use these two neglection linear regression, logistic.

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It is equal to pointy.

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It does the learning barometer, which decide to learn in grade two, either can have values between

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zero to one.

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And if you look at the help section, it is telling you that Iida control seed learning read.

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So if you put a very small value, it would mean that you need to run the model for more number of eight

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so that it learns completely.

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If you have large value of reader, that is learning is very fast.

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You can reduce the number of round.

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But in that scenario, the model may not be able to completely fit the data.

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So it is preferable to keep a lower value for ETR and a higher value of and drone so that your model

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fits the data completely.

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Nonetheless is a parameter which is specific to this objective.

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That is, since it is a multiclass classification objective.

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We need to specify the number of classes in that objective variable.

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Since outdoor activity will has only two classes, so class with you will actually well, you have to

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max depth control the growth of the three.

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So Max depth value of hundred means that the final three has can have a maximum depth of hundred levied

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by a default.

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It has a maximum depth of six.

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So I'll run this, come on.

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So for the 50 round, those who have dehydration that complete and the value of a booze model are stored

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in this Ezzy boosting variable.

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Now, using this extreme boosting variable, we will predict devalues and there's a zip rate variable.

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So we will again use the predict function, exhibiting the model name X Matrix, underscore P is for

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the test set.

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So this is the D matrix of the test set.

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If we run this timeline, the predicted values of the glasses is in the x deep red variable.

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Now to see the prediction accuracy of this model, we will run this table.

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Come on, let's create a conclusion matrix.

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And you can see that our model is correctly classifying these two sets of observations, that is 31

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had predicted values, although zero and actual value was also zero or false.

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And these 43 were predicted as one and actual results one.

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So far, 74 out of 113.

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We are getting late predictions on that test.

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So 74

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out of 13 is correct.

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So basically, we have a prediction, accuracy of sixty five point five percent.

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We can change the parameter values to get different prediction accuracy.

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So probably if I decrease the depth to in.

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And then this mortal again.

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And now I predict devalues.

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I know what they did.

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They will.

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Now I'm correctly predicting seventy one cases instead of 70 focuses.

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So reducing the depth in this scenario has reduced the accuracy of my model.

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So you can check it for different values of and grown different values of Iida, different values of

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Max stepped and seedy prediction, accuracy and different values of different parameters.

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If you know how to do looping, that is if you know how to run loops and the software, you can also

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run a loop and find out the different prediction accuracies at different values off and on.

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So probably you can run a loop on and on changing its value from 10 to under.

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And for each scenario you find out deep prediction, accuracy, wherever you get the best prediction,

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accuracy.

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You keep that value of Hendron.

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That is something we will not be covering in this course.

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However, that is also possible.

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So this is how we do X GS boosting.

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And ah, you have seen that initially when we created very simple busy entries.

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We could plot them.

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We could easily interpret them.

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And we can use those visuals in our presentations easily.

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They basically were very interpretable.

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But to increase the prediction accuracy, we traded off that in dependability and regard prediction,

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accuracy.

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We talked about on timbale method, bagging random forest and boosting in boosting.

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We further discussed gradient boosting Adam hosting an extra boosting.

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All of these methods showed great improvement in prediction accuracy as compared to a simple decision

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tree or a prone decision tree.

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So now you know both parts.

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How to create the simple decision tree and how to create a very advanced Gaudette prediction.

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This is tree.