Now, usually we do not cream our model on all the available data.
We take a small portion of our available data to test.
So what are more than.
This is small does it will help us to estimate how our model will perform on the real life data?
The data, which is not used to screen our more than.
So in practice, we generally use 80 percent of our available data and we take 20 percent of our available
data as testing data.
The only test so more than on their data and compare the performance of our different models on that
test data to evaluate our models on real live data.
So we have five hundred and six rows.
That is our available data.
We will keep 20 percent of this data as our test data and we will train our model on the 80 percent
of this data.
This segregating of data and two best and Krien part is known as the screen split.
And it is very easy to perform test strength is split using a Skillern will force import train underscored
Besse underscored split from Escalon more than selection.
Now, this train underscored tests, underscored split matter.
Take this barometer's, which is what X will do, our Y value the size of our test data.
Since I have told you that you usually get 20 percent of data as our test data, so you can provide
zero point two here.
Then there is another parameter that is random ESADE.
Since we are randomly assigning our data and to test and train to get the same test data every time
so that we can compare performance of our model, we can use this and demonstrate variable.
This is just a random number.
You can take zero one or any other value you want.
The advantage of using this round number suit is.
If I keep this right, no mistake concern throughout my analysis, I will get the exact same split off
as screen.
So even if you are running, your testing is played with three a.m. to zero, you will get that same
test strain split as me.
For example, you have 10 rows and suppose you are third and fourth rows are going in to test.
And rest of the eight groups are going in to train data dataset.
If you keep that random is constant, you will always get your third and fourth value as best set and
dress of values in your train set.
And this will help us to compare the performance of our model across different methods and to keep the
output of our model always concerned.
So always stick to a single value of no.
Don't change this value.
Stick to the number of your choice if you want to get the same split as me.
Select random insert equal to zero.
Now we get four outputs from this function.
The first output should be more extreme.
So I have named my variable as X underscore crane.
The second output is your best X data.
I have named it X and School Crane.
Then we have Y of school Crane and VI underscore test.
So if I run this.
I have four more variables.
I can check the head.
Awful lot extreme.
And the shape of the extreme due to.
You can see December looks exactly same as the sample from our X date frame.
We don't have any way variable here.
We only have X.
And one thing to notice here are this indexes.
You can see our indexes are sharp for long since some of the rules are going in to screen test data
and some of the observations are coming into our external data, we can check the shape of our extreme.
This should contain 80 percent of the values or for all the values, that is 506 and 2.8, which comes
out to be four hundred and four.
So we have four hundred and four values in our extreme and we have a test of the values and our X test.
So a hundred and two values and test and 400 Ford values in Crane total.
We have 506 observation will use this extreme to create our model and we will use X test.
To evaluate performance of water, Martin.
Similarly, you can check the shape of future via Train and Vytas.
The normal observation should be seen as extreme and expressed, respectively.