Now, in this video, we are going to bring the model with a radial cannon as we go and indeed your
elected radial camel as to hyper parameters.
One is the gamma.
Which 60 days later, importance of the distance between the retail company and other point.
And the second hyper barometer is the cost.
This we have been discussing since Línea comment.
So fitting that a deal cardinal is the same.
It uses the SBM function only that the cardinal value will be radial here instead of linear or binomial.
We will store this in SVM fit.
Ah, it will be for radio.
And if I run this.
Come on, you can see that I have a similar SBM print on variable which has the information of radial
gunnin as a model.
Now you can use this.
Variable to predict the values on the best set and take out its performance.
But it does not really make sense because we need to find out that value of these hyper barometers at
which we expect that the test performance will be better.
So we want to find out the value of glamour and cost.
Or that we will use the function and we will give it these values of cost.
You can see that I have provided logarithmic range.
It start from zero point zero zero one, then zero point zero one and goes up to 1000.
And these values of Gama let from Datapoint to one to 50, I'll reduce some of these, I'll remove this
to this forward so that it does not take too much time while running.
And I've also added this glossy article to Ford.
Which means that there should be full cross validation sets instead of thing.
So everything is the same.
Just got radial.
And in the hybrid barometer's, instead of the degree we have Gamma Gloucester's the same as earlier
and we have a cross-validation take out for.
So I Londis, come on.
And we have this tuned out out of what evil created, this video will condense the information of all
the combination of models that could be created using these values of cost and gamma.
If you want to look at the information in this, you're not out.
Out.
You can go.
Somebody come on on this and you can see that it contains the information of all the different combinations.
So for cost of this, Garmo, of this, we get a cross ventilation at add value.
This.
And so on.
So all the different models that ran across cross-validation error values did this best model variable
of do not old are contains that particular combination of cost and darma, which has the minimum crossway
religion in it.
So I run this command to store the information of that best model.
Invest more are.
Now, to look at this best mark up and underscore man, and you can see that we ran a radial SVM.
The cost at which we got minimum cross-validation error is Cees equal to 10.
The number of support vectors are coming out to be and a 19 out of three eight one sixty two of them
are on one side, 157 out on the other.
And we have a total accuracy of nearly sixty one point eight.
It's a four fold cross-validation.
Now using this model.
We are going to predict the values on the deficit and those values would be stored in wiped out.
And now we are going to compare how our predicted values fared against the actual values in the essay.
So we'll run this.
It will.
And you can see that we are correctly predicting these 29 values for which we predict that Oscar will
not be given to the movie.
And actually the movie did not get the Oscar.
And for at least 37 movies for which we predicted that it will get an Oscar.
And actually, it the.
So we get an accuracy of 66 out of when they do it in percentage terms.
That is 66 divided by one, they do it.
It is nearly sixty one point one percent.
So you can see that when we then are polynomial Cottonelle also, it was suggesting that there is more
of a linear relationship between the predictive variables and the response would even.
And when we tried to feed a canal, cardinal swim on variables, we tell more of a linear relationship.
It does not perform better than the polynomial order linear cardinal.
So using the right gomel is very important.
If that is the new relationship, a linear cardinal will do better.
If there is a non-linear relationship, really a lot more normal cardinals would do better.
And this was all about classification.
That is that the response variable of the variable that we were trying to predict had glasses and we
were assigning glasses using the created model on a test set or a music.
In the next video, we will learn how to do regression using SBM.
That is how to predict continuous values instead of classes using the SBM technique.