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Hello my

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friends, I hope you digested
well the previous tutorial where we tackle

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this big but yet important
tool of our data preprocessing toolkit.

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Indeed.

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Now you know how to handle the case
where you have some categorical data

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in your data set, which is a situation
you will encounter many times

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in your future machine learning career.

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And now we have two tools to cover,
the first one being splitting

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the data set into the training set
and the test set,

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and the second one being feature scaling.

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So before we start, I'm about to answer

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one of the most frequently asked questions

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in the data science community,
which is be ready for it.

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Do we have to apply feature scaling
before splitting

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the data set into the training set,
and to set or after?

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I've seen this questions many times
and you will find

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that question in many forums
of the data science community.

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Some people will say that we have to apply
feature scaling before the split.

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Some people will say after the split and
now I'm about to reveal the right answer.

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There is only one right answer,
which is, by the way, totally obvious.

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After you get the explanation.

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So the answer is
we have to apply feature scaling.

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After splitting the data set
into the training set and the test set.

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And now let me explain.

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So first
just to make sure everybody understands.

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Let me explain the what first.
And then I'll explain the why.

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So of course splitting the data
set into the training set and the test.

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It consists of making two separate sets.

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One training set
where you're going to train

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your machine learning model on existing
observations, and one test set where

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you're going to evaluate the performance
of your model on new observations.

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And it's important to understand that
these new observations are exactly like,

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you know, some future data
that you're going to get and on

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which you're going to deploy your machine
learning model.

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All right.

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So that's this first tool.

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And now feature scaling simply consists
of scaling all your variables,

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all your features actually to make sure
they all take values in the same scale.

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And we do this so as to prevent
one feature to dominate the other,

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which therefore would be neglected
by the machine learning model.

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All right.

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So that's the what
for both of these tools.

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Now let me explain the why
we have to apply feature scaling.

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After splitting the data
set into the training set and test it.

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It's really obvious.

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It is for the simple reason
that the test set

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is supposed to be a brand new set

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on which you are going to evaluate
your machine learning model.

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So it's exactly like, you know,
your training, your machine learning model

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on your training set, and then later
on, you know, after it is trained,

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you're going to deploy it
on new observations.

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So what this means is that the test
set is something

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you're not supposed to work with
for the training.

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And feature scaling
is as you will see, a technique

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that will get the mean
and the standard deviation

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of your feature,
you know, in order to perform the scaling.

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So if we apply feature scaling
before the split,

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then it will actually get the mean
and the standard deviation

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of all the values,
including the ones in the test set.

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And since the test set is something
you're not supposed to have,

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you know, like some future data
in production, well, you know, applying

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feature scaling on the original data
set before the split would cause some

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what we call information
leakage on the test set.

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You know, we would

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grab some information from the test set,
which we're not supposed to get

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because it is supposed to be new data
with new observations.

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So remember this the essential reason
why you should not apply feature scaling

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before the split is to prevent information
leakage

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on the test set, which you're not supposed
to have until the training is done.