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In this lecture, we will learn how to split our dataset into two parts test and train data set.

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Then we will train our logistic regression model on training dataset and we will evaluate the performance

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of that model using the test dataset.

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I have already written all of the codes, my data going new to write all of these quotes on your own

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while you are practicing.

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First, to split our data into test and.

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We need this function green test split and you can import this function from Escalon, not model selection.

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The output of this function is in the form of four variables.

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First variable should be your independent green data.

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Second variable should be you're independent based data and then you are dependent on data and dependent

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test data that are for argument for this function.

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The first one should be the independent variables from your or is no other doorframe.

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We have already created X variable.

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Therefore, we are using X.

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The second argument here is the dependent variable.

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We have already created Y.

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That's why we are using Y.

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Then the next argument is further test size.

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So as we discuss in our two Reflektor ideal split for this strain, is it being 20 percent or two percent

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for Crean and 20 percent for test?

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And here you can mention the portion of your whole dataset you want in your test data.

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Since we won 20 percent of test data, therefore we will read zero point two.

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If you want to do percent of tests today, you can read zero point three and sort of zero point to the

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next argument is for random assert.

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Here you can provide any value.

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But if you grow eight zero, you will get the same desert as I'm getting from this randomly split.

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If you write one or any other value, this function will work exactly the same.

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But the only difference will be that you will not get the exact same result as me because you are random

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numbers will be different from my random numbers.

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So if you want to get the same split every time, you'll have to stick to only one random state.

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No, I'm using zero.

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And for all future test this state, I will use zero

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not initially in our X and Y variable.

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There were five hundred and six rows.

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Now let's look at the shape of extreme expressed by train and by test.

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If you are using updated version of Biton, you need to put parenthesis with the print.

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Come on.

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I feed on this, you can see that 80 percent of data is an extreme.

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And the remaining 20 percent is in its best 404 observation are an hour extended to and under.

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And two observations are in order.

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Experts data.

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Now, let's fit a logistic regression model on our training dataset.

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We'll follow the same step.

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First, we will create the object.

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Seacliff underscore a lot and then we will fit our extreme and white dream in this object.

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We have fitted our model.

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Now let's predict the value on what X test does say.

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You can notice here I am using expressed and sort of extreme because I want to credit the values on

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my test data set.

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Now let's look at the accuracy and the confusion metrics of this model to get the accuracy score and

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confusion metrics.

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We will import these, too, from Escalon metrics.

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We will run it, then will clear the confusion metrics for our via test and why test predict.

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These are the actual values of our white test.

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And these are the predicted value of a lot of test.

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Here are what rules represent.

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The actual class and the column represent the predicted class.

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So for the statistics, no, we are seeing that the actual value was not sold.

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And the predicted value was also not sold for this 36 records.

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If you remember, these are known as crude negatives.

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Similarly here, our actual value is one.

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But the predicted value is zero.

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These are known as false negatives.

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And for this 22, the actual value was zero and the predicted value was one.

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These are known as false positives.

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So when the ladies are positive.

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Now, the accuracy score is the percentage of observations.

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My model is able to predict correctly, so the accuracy score will be the sum of 36 plus 31 divided

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by the total number of observations.

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You can do that manually also, but there is a small function to calculate it phonetically.

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The accuracy of forward model on our test dataset is zero point six five.

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It's not great, but it is good enough.

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And it is always coming day to use your test data set to create the confusion metrics and to calculate

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the accuracy score.

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Now I have calculated this accuracy score and confusion metrics for logistic regression for a.

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You can grade on your own and you can also compare the accuracy score between the two models and the

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next video.

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We will learn how to create game and model and Biton.