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So in the last video we created over Libya, as we are more than for sequel to zero point zero one.

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But how to ensure that this is the best sea which we can choose for our linear model?

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We can provide any value of sea and for each value of sea, we will get a separate model and the accuracy

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escort will be different for all such models.

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So one way is to manually change this sea so I can run the same model with sequel to one with sea equal

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to zero point one and with sequel two hundred and manually compare the accuracy results for all such

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models.

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This is a lengthy process.

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To overcome this lengthy process, we can use grid search option of a scale on here.

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We can provide.

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Different values of SI.

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And Biton will automatically run our model with all these values of see.

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And later on, we can use the best model out of this given models.

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Could do this.

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We have to use grid search c.v.

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So first we have to import grid search seewhy from Escalon modern selection.

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Now, next step is to create a dictionary in which you have to mention the name of your hyper parameter

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as a key.

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And the value it will be taking as a values of your dictionary.

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Here, since I want to change values of SI.

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I will provide see as a key and then these are the values.

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I am passing this values in the form of poll.

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You can see I have selected values from zero point zero zero one to tosing.

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So I will run my model for all these 13 values of see.

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If I want to change and the other hyper parameter, I can just broyd another key value pair.

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Now, we have created a weather parameter dictionary.

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The method of using grid search is almost similar to that of training a model.

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There is one additional step.

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First, we need to clear the model that we are going to use in grid search.

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So suppose if we are using support vector classification, we have to create an object for support vector

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classification, a field using linear regression.

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We have to create an object for linear regression.

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If we are using support vector regression, we have to create an object for support vector regression.

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So first, the surplus to create that object.

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Then just like any other model training, we have to provide parameters and grid search.

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The first parameter is the word model.

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Then the second parameter is the list of parameters that we have already created.

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Note that this should be in the form of a dictionary with the parameters of your model as scheiße and

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their values as values.

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Then there are a few other parameters.

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The next parameter is and jobs.

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This should be equal to minus one.

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This is used to maximize the performance of your laptop or machine.

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So minus one means your computer or your system will use all the processing power for training this

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model.

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Then the next parameter is CV.

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CV is for cross validation.

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So if you don't know what cross-validation is, Biton will create in our model.

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All this will lose of C.

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And then it will automatically use a part of of, uh, training data to get their test result and find

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out the accuracy scored on this many values of parameter.

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So CV is ten here.

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You can change this number if you want.

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Then verbals is equal to one.

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This represents the amount of message we are going to get.

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While we are training or model.

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So you saw that we received messages in our output.

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So a few give.

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What was it?

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Well, the one you will get some message while you're cleaning your data.

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And the next parameter is scoring.

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Here we want to use accuracy scores of this models.

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Therefore, we are using scoring well to accuracy.

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Then the next step is just like any other model.

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We will fit this grid search object using our test, Sandrine.

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And after that, since we are training this many models, we can pick the best model and then predict

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the values of my variable.

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So let's first import the grid search.

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Cleared this paramedic study.

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Cleared this VLF, a solemn, linnear object.

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You can see that we are just using as as we see with current liquidly, too linear.

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And then.

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We are creating a of grid search object.

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If you want more details on the parameters.

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You can open the documentation of Eskil and Grid Search.

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You will get all the information there as well.

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Then the next, the surplus to Fethullah data.

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Remember to use standard X data instead of just X variable.

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Now, you can see we are getting this kind of messages.

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This is because we have used verbal sequel to one.

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You can see we have provided turbin values of see, that's what we are getting, the message that we

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are fitting then for we model for 13 candidates.

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For you to send for cross-validation, equate to 10.

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This may take some time.

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You can see that we have fitted our models now grid search comes with different attributes.

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The first attribute here is best underscored patterns.

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This will give you the information of the best model.

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Although this 13 models.

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So if freed on this, we can get the parameter for which we are getting the best result.

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So out of all this sea values, we are getting our best preserved at sea.

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Well, two zero point five.

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So you can see that there is no need to separately run.

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You are more than for each value of sea.

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You can just provide a single Padam dictionary and grid search, do random order for all the values

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of sea that you have mentioned in badam parameter.

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The next secretary butyrate is best under score, estimate estimated.

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So with best underscore Perram, we were getting the parameter value corresponding to our best model,

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that best center scored Sumita.

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We will get our best model.

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And here we are simply saving this, our best model, as Len, as we underscore to see Alef later on.

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We will use this Lin as a swimmer, not to score CnF.

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To predict the values of this.

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Let's run this.

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So now we have a what best model?

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The model with sequel 2.5.

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And this variable.

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Now, let's find out the accuracy score.

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Here we can radically use why test and instead of providing a separate credit variable, we can directly

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use Daudt predict function here.

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So let's run this.

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The accuracy for our model is zero point five nine.

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So this is the best accuracy which we can get out of this 13 values since our data is very less.

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And we only have around 500 observations.

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That's why he a good service is not making much sense.

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And we may get accuracy score lower than our initial accuracy score.

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So don't worry about that.

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If you have large amount of data, grid search will definitely give you a better result as compared

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to some random value of parameter.

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That's so we use grid search in the next lecture, will green, a polynomial caramel SVM model.