In this video, we will learn how to train a little more than on the data.
First, we will import the linear discriminant analysis function from a Skillern.
Then we will fit the word beta into this model.
Then we will predict the values of Y.
And then we will create the confusion matrix to compare to the false positive or negative.
And all the four classes, not that we have created the template for this model.
And if you want to create this model on your own data, you just have to change the X and Y will follow
the same steps.
Creating a model, predicting values and creating confusion metrics.
Let's first embody linear discriminant analysis from ESKIL and or discriminant analysis.
We are important.
First, we will create this object.
CLV underscored clear object.
This is a linear square and analysis object and then will fit our X and Y into this object using a fit.
Come, I will run this.
Now we have four dead our model.
Now let's predict the value of Y from our model.
And we will save those values.
And by underscore Greg.
Underscore Alea.
Now let's show you the values that are restored and why underscore credit under this great idea.
You can see, though, put it in the form of zeros and ones.
One is sent for sorted and it was sent for NORAD.
Now let's create a confusion matrix.
We'll just read confusion metrics and then we'll mention the actual way and the predicted way.
This is our confusion matrix.
You'll know how to.
Confusion, matrix and that.
So we're done.
Alia and Biton.