Now, let's start building as swim regression, Morden and Biton.
I have provided you the links for oficial, a skill learned document of Astrium regulation.
You can click on this link to open the documentation.
Here you can find the syntax and the parameters of this function.
The first parameter here is colonel here.
You have to specify what Colonel you want to use by default.
This is R, b, f, r, b, if it sense for radial.
But you can also choose linear polynomial or sigmoid function, for our example.
We will use the linear cardinal.
The second parameter here is degree degrees sense for the degree of polynomial kernel.
Then we have gamma.
Gamma is only relevant and radial polynomial or sigmoid function.
Again, we have another parameter which is specific to polynomial.
That is coifs zero.
And then we have see, this is the penalty parameter C, which we have already discussed during over
two reflectors.
And there are some other parameters as well, which we will not use while creating our modern.
Now, these are the attributes.
The parameters are the variables you need to provide value training, your model and attributes contain
the information of the model that you have already trained.
So, for example, a few write object name and you will provide DOD support underscored.
It will give you the indices of the support vector.
Similarly, if you want to view all the support vector, you can just right, you are model, object,
name and dot support.
Underscore vectors underscore.
And it will give you all the support.
But that's.
So for each function, we have parameters and attributes in a scalar.
Now let's go back to our notebook.
So for every Skillern model, you have to follow the same astep.
First, you have to import the function that we are doing here.
Then we have to create an object.
This can be your regression object or classification object using the function that you have imported.
So here I am creating an object.
By calling this function as we are calling to hyper parameters, we want linear kernel.
That's why we are providing first parameter as cardinal, equal to limit.
And we want our Seawell you panel do a load to be tosing.
That's why we are providing sequel 2000.
All the other hyper parameters will use there before you will lose.
If you want to know the default value, you can open the documentation or you can just you can just
press shift tab inside your function.
First, we will run this.
Now, if I press shift PEB inside my function, I will get all the parameters and their default value.
In the next to step.
We will use our object to fit graining data into that object.
So since we have created our extreme standard variable, so we will use that variable and we will use
our wide-screen variable as it is.
So we will write as we are.
This is our variable that we have created earlier, and we will use Daudt Fit Method to fit our model.
If I run this now, I have fitted my model.
I can use this model to predict the values of Y.
Now I can predict Y values by using Daudt predict method of what object.
So we want to assign our predicted values in y underscored test.
Underscore red and white.
Underscore train underscore spread.
This will contain my predicted values on test data.
And this will contain projected values on our train data.
We can radically use as we are dort predict method and in bracket.
We have to mention the X variable.
So for our test data I am providing X underscored tests under the gold standard and for train data.
We are providing X underscore train underscore standard.
Let's run this.
Now I have my predicted values in these two variables to view our predicted values.
You can just state any of these two variables and executed.
You can see this are over dedicated values.
Now we want to know how our model is performing.
So we have already predicted our test and Kreen y values.
We will use these values to find meaning squared error and are square scored with a Skillern.
We get this to escort's.
We will import DESPUES scores and we will use our credit card values to find this escort's.
Now, if you're not aware, what I mean is squared error means it mean that it is the mean of the square
of difference between the word predicted values and actual values.
And if you want to know about what parameters do we need to execute this, you can just press shift
Tebbe here.
You can see the first parameter is via Google News.
Since our actual values are in via underscore test, we will.
Right.
Why underscore test?
And the word second parameter here is predicted values.
Since we have predicted our values and why underscore tests underscore bread, we will use this with
Able and define Londis.
The masc value on our test dataset is 160 million.
Now, the thing with MASC is you can only compare model performances on the same data.
So this are absolute number.
So you can only compare it with the masc value of some other model on this data.
You do not get any information by just looking at this number.
So the better option here is to use artist square values to evaluate the performance of more than.
Now, again, to look at the parameters that we need for our art to underscore its core function.
Just click shift tab.
And here also we need to put away the true value and the predicted value as our parameter.
We'll run this.
The oddest, good value on our training site, this point, seven one.
This is quite good.
Now, if you don't know about our a squirrel, you artist squirrel loose ranges from minus one to one,
one or minus one means perfect fit and zero means no fate.
In other words, one means your model is able to convey all the variations in your wide dataset, and
zero means your model is not able to convey any information for our Y variable.
So ideally you are discussing value should be between zero point five two zero point nine for a good
model.
For our test dataset, the artist good value is point five again.
This is good.
We can v.c of a sea value to increase our artist good value.
So if you want, you can just provide another Seawell, you suppose three thousand and creadon this
whole code?
You can see now our artists goodwell you on our training despond, so Anwan and artists get a value
on Ortez Sirtis point for me.
So I recommend you to try the value of see as 500.
And try to find out the artists good value for a sequel to 500.
From the next lecture, we'll move on to classification.
And there we will see advanced techniques.
To optimize this value of C to get the best results.