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So now that we have split the data into two parts and we are going to use green sea, did I say to bring

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the model?

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There is one important thing that you should note, as I told you earlier, that for now we are doing

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classification and later on we'll be doing integration.

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The function that we'll be using to run the Espey, a model will be seen for classification and regulation.

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However, that function identifies whether it has to then diggnation or classification basis, the type

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of the dependent variable that is the variable that we are predicting.

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If that variable is categorical, then it will do classification.

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And if that variable is numeric, it will do regression.

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So if you look at my treinta dataset.

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We go to the right.

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This tartikoff skirt is a dependent variable.

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The city variable that we want to predict.

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But if I hover over the name of this variable, you can see that it has numeric values with range zero

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to one.

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If I use this variable as many Brenin variable, the SVM function will see that this is numeric and

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it will run a regulation.

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So it is important that we change this to a categorical variable which are identified as factors.

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So we need to run this these two lines, which is converting the same numeric data into factors.

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So if I run this command and go and look at the Trinity variable.

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Daintily does it?

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Now, if I scroll, the starting Oscar variable is no factor.

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We live IDs do level dirty to anyone.

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So this is no categorical variable.

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Same thing we will do with test, see?

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And both of these data sets.

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Now has the starting Oscar variable as factor.

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Now this variable can be used to do classification.

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So let me want to SBM know to run SVM.

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We need to use this package.

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The package is called E one zero seven one.

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Most probably you don't have this package installed.

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You need to install these packages, as I showed you earlier, to install a package.

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We need to write install lock packages and within the brackets and single code will write the name,

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which is E one zero seven one.

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So this package is downloaded and installed to make it active.

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We will run the library command and the packages active also know we can use this package.

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So the brain and SVM model, we use this SVM function.

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This is Veum function as part of this even zero seven one library.

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We will store the output of this as function into this as we unfit variable.

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So SBM function takes all these parameters.

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The first barometer is the formula.

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And this we need to specify which is the dependent variable and which are the independent variables

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which are to be used as predictive variables.

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So this telecine, you can find this dealer saying.

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On your keyboard, above the tab key.

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This delay sign is used to separate dependent and independent variables.

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Anything to the left of the delay is the dependent variable.

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And all the independent variables that you want to use to predict this variable will be on the right

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of daily.

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So I want to predict Star Trek Oscar.

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So that is on the left on.

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Billy.

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And I want to use all other variables from my dataset.

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That is why I have put our daughter.

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So to represent that, I want to use all the variables as predictive variables, I have water don't.

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If I want to use only one variable, say, budget, I go to have written budget here.

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If I wanted to use two variables, I could have written budget plus time taken.

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So using plus symbols, I can add multiple independent variables here.

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But since I want to use all the other variables and it does not make sense to write the name of 18 other

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variables here by using plus symbols.

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We have just bought our daughter here, which signifies that we want to use all of the valuables.

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The second parameter is data, which is to be used.

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The data did train see dataset.

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Third parameter that we are giving is cardinal as we have Govardhan are two reclass.

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The linear gomel SVM is the same as support vector classifier model longto.

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So turning this come on will be same as running a support network classifier.

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Also, this is a linear support with the machine.

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So here we are using a good analogy called to linear parameter.

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The fourth barometer is the cost barometer, as they discussed in the two re-elected, we use budget

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or cost as I put parameters so that we control the rate of the margin and we allow some of the point

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to be misclassified to this cost is equal to one is giving us the cost of misclassification.

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The last perimeter that we are using is skill.

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We have four skill is equal to two, which means that we will be scaling all the variables in our dataset

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when we scale the variables.

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We change in values so that they have a mean of zero and the standard deviation of one.

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We do this because SVM is skill sensitive.

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What this means is if you have a variable in your dataset which has a very large value, say it is in

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millions and there is another variable in your dataset which is very small.

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So it is of the range of point zero zero one two point zero one.

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Now, when we use these two variables, since as we will be calculating distance, the variable which

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has very high scale, will be given more importance.

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Another issue that you will face in a scale since the model is, for example, if you have currency

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in your dataset, if you have one dollar and the data set, it will be considered differently than if

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that same value is in euros or in rupees or yangs and so on.

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So we have to make this data scale indifferent.

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To do that, we use scale is a goal to prove very few times the data does not need scaling in such a

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scenario.

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We can use scale is equal to faults also.

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You can add few more parameters.

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Also, if you click anywhere on dysfunction and press F1, you will see that the help for this function

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opens up.

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And in this function you can see all the argument that you can give.

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So I've discussed the important ones deform large data, scale Connel, etc. This type argument will

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be chosen by default.

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So as I told you, depending on the type of are dependent variable, that is where there is.

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It is numeric or factor.

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That type is elected, whether it should be classification or regression.

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So if you want to specifically give the tape, you can also specify that.

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So all these are arguments available.

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But I suggest that we use all these arguments.

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You can check out the other arguments also.

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These are the ones that you must know about.

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So I'll run this.

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Come on.

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And you can see that as we in fact, variable is now created and as we unfold, contains the information

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of the SVM model.

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If you want to get a summary of the information in this model, you can run the summary command.

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So somebody within brackets, we write the name of the model and.

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You can see.

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That we ran a classification type of model with a Carmeli near the Costa said one.

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We have three and therefore support wetters out of the 398 observations in Deep Ringley desert.

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So the margins that this model created within those margins, we have three hundred four point nine

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out of these three hundred four point one, fifty four point are on one side and 150 points are on the

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other side of the hyper plane number of classes to deliver that data in one.

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So this is the summary of the information in this as a model.

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So now that we have trained the model and the information is stored in as we in fact, we can check

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its performance on the test to Geddie performance.

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We will first use this model and the independent variables in the test dataset to get the predicted

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value on those observations and data set.

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Then we will compare these predictions.

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What is the actual value in these tests?

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It.

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So this first line, which is why bread is equal to predict SBN fit Comac Bessie is storing the predicted

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values in this whitebread variable.

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The values that are predicted using this predict function first barometer to this function is the model

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name.

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And the second parameter is deep test set.

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Here, you need not provide, which will be the dependent variable and which will be the independent

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variable, because in the model we already know, which were the independent variables, it will automatically

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take values of those independent variable from this day said.

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And using those values of independent variables, it will predict the values.

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Of dependent variable.

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And it restored them into widespread value.

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But.

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Now, the actual value is in STC dataset.

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Indeed.

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Star Trek Oscar variable, the predicted values are in my bread variable.

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To compare these predictions against the actual values, we use this table function.

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And this table function on the rules.

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We will get the predicted values and unbe columns.

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We will get the actual values.

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Creating this table is also called creating a confusion matrix.

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Let me first rounders predict function to predict predictive values, and I will then on this day will

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function and you can see that the predicted values are on the arrows.

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So.

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What these 45 cases, my model predicted that these 45 cases will not get an Oscar.

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Out of the 45 cases, actually 32 did not get Oscar and 13 did get the Oscar.

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And my model predicted that 63 movies will get Oscar out, of which 37 actually got the Oscar and 26

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did not get the Oscar.

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This matrix is also called confusion matrix.

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This is used to compare the performance of classification models.

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So here you can see that 69 nine of the cases were correctly predicted by our model and 39 guesses were

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incorrectly predicted by the model.

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So if you want to calculate the prediction accuracy of our model, you can simply write.

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Sixty nine divided by one zero eight.

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Since we correctly predicted four sixty nine cases and we have one zero eight observations.

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Indeed.

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Essid.

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So if I run this command, it is telling us that we are getting an accuracy of nearly 64 percent.

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So using this model with an accuracy of 64 percent, I can predict whether a particular movie is going

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to get an Oscar or not.

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This last line here, which is, as we inferred, dollar index.

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This is used to check which of the observations are support vectors, since in this scenario we are

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getting a lot of observations.

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This may not make any sense, but if you have very few observations and out of which two or three support

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vectors, you may want to find out which of the observations are support vectors.

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Change your model completely depends on those few observations.

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So a slight change in any of those observations can change your model.

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So you may want to find out which observations are your support vectors to find out which observations

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other support workers will on this command, SBM three dollar index.

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And you can see here that first sixt well, 13.

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All these are the index of observations, which are your support vectors in this video.

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We have seen how to train the SBM model with a linear Cottonelle and we then.

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So how do you use that train model to predict values on a test set or a new set?

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And then we used those predicted values of dataset to find out deep prediction, accuracy of our model.

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And lastly, we also saw how to find out which of these observations are the support vectors.

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In the next video, we will see how to find out that value of this hybrid parameter.

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This cost parameter.

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We want to find out such a value of this cost parameter, which gives us maximum accuracy.

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So we will learn how to tune this hyper parameter.