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Now, let's start building as swim regression, Morden and Biton.

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I have provided you the links for oficial, a skill learned document of Astrium regulation.

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You can click on this link to open the documentation.

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Here you can find the syntax and the parameters of this function.

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The first parameter here is colonel here.

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You have to specify what Colonel you want to use by default.

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This is R, b, f, r, b, if it sense for radial.

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But you can also choose linear polynomial or sigmoid function, for our example.

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We will use the linear cardinal.

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The second parameter here is degree degrees sense for the degree of polynomial kernel.

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Then we have gamma.

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Gamma is only relevant and radial polynomial or sigmoid function.

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Again, we have another parameter which is specific to polynomial.

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That is coifs zero.

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And then we have see, this is the penalty parameter C, which we have already discussed during over

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two reflectors.

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And there are some other parameters as well, which we will not use while creating our modern.

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Now, these are the attributes.

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The parameters are the variables you need to provide value training, your model and attributes contain

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the information of the model that you have already trained.

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So, for example, a few write object name and you will provide DOD support underscored.

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It will give you the indices of the support vector.

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Similarly, if you want to view all the support vector, you can just right, you are model, object,

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name and dot support.

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Underscore vectors underscore.

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And it will give you all the support.

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But that's.

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So for each function, we have parameters and attributes in a scalar.

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Now let's go back to our notebook.

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So for every Skillern model, you have to follow the same astep.

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First, you have to import the function that we are doing here.

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Then we have to create an object.

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This can be your regression object or classification object using the function that you have imported.

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So here I am creating an object.

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By calling this function as we are calling to hyper parameters, we want linear kernel.

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That's why we are providing first parameter as cardinal, equal to limit.

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And we want our Seawell you panel do a load to be tosing.

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That's why we are providing sequel 2000.

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All the other hyper parameters will use there before you will lose.

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If you want to know the default value, you can open the documentation or you can just you can just

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press shift tab inside your function.

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First, we will run this.

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Now, if I press shift PEB inside my function, I will get all the parameters and their default value.

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In the next to step.

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We will use our object to fit graining data into that object.

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So since we have created our extreme standard variable, so we will use that variable and we will use

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our wide-screen variable as it is.

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So we will write as we are.

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This is our variable that we have created earlier, and we will use Daudt Fit Method to fit our model.

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If I run this now, I have fitted my model.

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I can use this model to predict the values of Y.

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Now I can predict Y values by using Daudt predict method of what object.

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So we want to assign our predicted values in y underscored test.

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Underscore red and white.

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Underscore train underscore spread.

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This will contain my predicted values on test data.

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And this will contain projected values on our train data.

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We can radically use as we are dort predict method and in bracket.

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We have to mention the X variable.

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So for our test data I am providing X underscored tests under the gold standard and for train data.

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We are providing X underscore train underscore standard.

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Let's run this.

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Now I have my predicted values in these two variables to view our predicted values.

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You can just state any of these two variables and executed.

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You can see this are over dedicated values.

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Now we want to know how our model is performing.

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So we have already predicted our test and Kreen y values.

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We will use these values to find meaning squared error and are square scored with a Skillern.

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We get this to escort's.

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We will import DESPUES scores and we will use our credit card values to find this escort's.

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Now, if you're not aware, what I mean is squared error means it mean that it is the mean of the square

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of difference between the word predicted values and actual values.

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And if you want to know about what parameters do we need to execute this, you can just press shift

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Tebbe here.

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You can see the first parameter is via Google News.

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Since our actual values are in via underscore test, we will.

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Right.

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Why underscore test?

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And the word second parameter here is predicted values.

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Since we have predicted our values and why underscore tests underscore bread, we will use this with

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Able and define Londis.

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The masc value on our test dataset is 160 million.

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Now, the thing with MASC is you can only compare model performances on the same data.

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So this are absolute number.

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So you can only compare it with the masc value of some other model on this data.

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You do not get any information by just looking at this number.

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So the better option here is to use artist square values to evaluate the performance of more than.

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Now, again, to look at the parameters that we need for our art to underscore its core function.

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Just click shift tab.

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And here also we need to put away the true value and the predicted value as our parameter.

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We'll run this.

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The oddest, good value on our training site, this point, seven one.

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This is quite good.

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Now, if you don't know about our a squirrel, you artist squirrel loose ranges from minus one to one,

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one or minus one means perfect fit and zero means no fate.

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In other words, one means your model is able to convey all the variations in your wide dataset, and

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zero means your model is not able to convey any information for our Y variable.

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So ideally you are discussing value should be between zero point five two zero point nine for a good

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model.

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For our test dataset, the artist good value is point five again.

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This is good.

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We can v.c of a sea value to increase our artist good value.

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So if you want, you can just provide another Seawell, you suppose three thousand and creadon this

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whole code?

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You can see now our artists goodwell you on our training despond, so Anwan and artists get a value

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on Ortez Sirtis point for me.

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So I recommend you to try the value of see as 500.

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And try to find out the artists good value for a sequel to 500.

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From the next lecture, we'll move on to classification.

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And there we will see advanced techniques.

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To optimize this value of C to get the best results.