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Now, let us learn how to run a linear discriminant analysis.

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So to create an elite classifier, we need a particular package.

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This package is called Mosse.

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If you search these packages, you will find a package called Mosse, which is already installed in

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it.

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You just need to take it.

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Or you can add eight library.

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And within Blackard, you can specify mass.

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After this, we just need to use the Alea function.

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This is same as DDL in function, which we used to order deterioration.

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So we will create a variable called a leered outfit.

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This variable will contain the information of our model and we'll use the Alea function, sword is the

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dependent variable.

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The lead dog dart signifies that all the other variables are do you are to be used as predicted variables

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and details if run this.

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So you can see another variable created, which is a lead outfit, just like Delimiter Quick.

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If you want to look at what is the information contained inside a lead outfit, you just write a lead

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or quick.

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I'm going to make.

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This first part is the prior probability of the group.

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That means out of under observations initially.

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Fifty 54 belong to the 08 category.

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That is 54.

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Houses were not sold out.

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A hundred and forty five were sold out of hundred.

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So this this is giving us the initial probabilities of these two groups in the response variable.

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These are group means related to each of the individual predicted variables.

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And here we have decompressions of linear discriminant.

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But we do not want to look at this result.

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We straight away want to look at the probabilities, the predicted probabilities and the predicted losses

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for our new dataset to find out the predictive probabilities.

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There's a predict function.

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So let us assign deplorable Digitas, indeed predict function.

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We will create a variable called a lead or spread.

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And this will be information from predict function.

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So will rate predict.

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The first parameter will be a lead outfit.

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And the second parameter will be the dataset.

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So right now we are predicting B values on the same dataset, which is data B if.

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Do you concede a leader?

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Greg is also created?

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It is.

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It has three list.

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If you want to look at this, we will just click on it.

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You can see it has Thrillist, Bossidy, glass, glass, as he predicted.

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Glasses assigned to each of the written.

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And it is assigned on the basis of our default value.

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Point five.

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That is the boundary convictional is point three.

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The second list is called posterior.

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It contains these two probabilities.

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One is of that observation belonging to the first group.

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And the other is of that operation going into the second group.

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So in our case, we have two groups, zero and one.

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So first column of people seated represent the probability of that observation belonging to the group,

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a total detail.

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And the second column represents the property of that of the reason belonging to group Title one.

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So let us look at the posterior probabilities by writing LDA Dark

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Dollar Posteriors.

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So there you can see for all the 506 observations, we have two columns, zero and one if are independent

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variable, add more than two columns.

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We would have more than two columns here and the probabilities of that probability blowing to each group

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will be available with us.

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So here you can see what the first observation, the probability of belonging to zero is point to and

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probability of belonging to one is point eight.

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So basically the addition of probabilities along every row will come out to one.

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So in the first column, we have class.

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If you want to use the default boundary condition of point three, you can straightaway used the class

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variable so you can assign a Leonhard class is equal to.

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A live dot, the glass.

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So elite or class as these predicted classes or each or if I wonder, six observations, beef dollar

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soared, has the actual classes or if I wouldn't say subdivision's, then we have predicted values and

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the actual values we can create confusion, matrix to create confusion, metrics.

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We will do the same thing as we did in logistic regression.

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We will like table.

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Then the first barometer is what we want in Rose.

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We keep a lead or class.

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And the second barometer is what we want in columns, which will be beef dollar sold on this.

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And you can see the bottom left corner.

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We have the confusion metrics.

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You can go back to the logistic regression lecture and compare the confusion, matrix of logistics,

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metric versity element of.

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Now, if you want to change the boundary condition while assigning classes, you can do it using the

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posterior column of these predict function.

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So I tell you how to get the number of classes which belong to one.

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If we take about recommission of point eight.

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So we will read some.

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And within decades, a leader.

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Bread, dollar, posterior.

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In square one.

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Blank, comma, one.

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Is better than point eight.

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Let me explain this to you, eldest daughter.

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Fred had three columns out of those three.

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We want the posted column.

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Posterior itself has two parts.

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The first is containing the properties of the glass zero and the second column is containing the property

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of Class one.

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I'm saying that wherever the classes one and the posterior probability is more than point eight, give

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me the count of all those values.

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So if I run this.

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I get 76 so far.

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76 observations.

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The predicted probability of belonging to Class One is more then pointed.

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Lasting is harder than a quadratic discriminant analysis.

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Is similar to LTA.

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Only thing is, instead of a layer here, we will change it to Kudi.

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So this is all with an Kudi.

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You can do the same things, just change security ahead and cleared this confusion matrix with Yudi

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and combatted performance.

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And this is a homework where you.

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I'll summarize it quickly first for demurral using the Aliev function.

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The difficult information you can predict the probabilities this predict function as by default classes

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assigned using a bond degradation of point five.

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Or you can use the posterior column, which contains the probability to assign losses.

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This is some other boundary condition that in linear discriminant analysis.