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In this video, we will learn how to train a little more than on the data.

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First, we will import the linear discriminant analysis function from a Skillern.

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Then we will fit the word beta into this model.

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Then we will predict the values of Y.

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And then we will create the confusion matrix to compare to the false positive or negative.

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And all the four classes, not that we have created the template for this model.

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And if you want to create this model on your own data, you just have to change the X and Y will follow

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the same steps.

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Creating a model, predicting values and creating confusion metrics.

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Let's first embody linear discriminant analysis from ESKIL and or discriminant analysis.

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We are important.

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First, we will create this object.

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CLV underscored clear object.

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This is a linear square and analysis object and then will fit our X and Y into this object using a fit.

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Come, I will run this.

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Now we have four dead our model.

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Now let's predict the value of Y from our model.

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And we will save those values.

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And by underscore Greg.

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Underscore Alea.

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Now let's show you the values that are restored and why underscore credit under this great idea.

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You can see, though, put it in the form of zeros and ones.

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One is sent for sorted and it was sent for NORAD.

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Now let's create a confusion matrix.

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We'll just read confusion metrics and then we'll mention the actual way and the predicted way.

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This is our confusion matrix.

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You'll know how to.

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Confusion, matrix and that.

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So we're done.

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Alia and Biton.