Now, let's continue with our project.
In this video, I want to do some scouting, so analyzing the rapid loss we have not not enough.
I was happy for when the predictors have different ranges.
Namba on respon variables by feature, having a greater numeric range could be more than the one having
a less dramatic range.
And this could in turn impact the prediction accuracy.
Our goal is to improve predictive accuracy and not allow a particular feature to impact the prediction
due to a large enumeration value range.
Hence, we may need to scale values and the different features that they fall under a common range.
Throughout this statistical procedure, it's impossible to compare identical variables belonging to
different distributions and different variables, all variables expressed in different units.
So remember, it's a good practice to raise carolita before training.
A deep learning algorithm with rescaling data units are eliminated, allowing you to compare data from
different location easily.
So in this case, we will do that mean maximize our usual con feature scaling to get.
On the scale data in the range from zero to one, so that try some formula for the.
Before that, I want to test.
So I scale.
Equal.
I mean, us, I mean.
Divide by.
Expedition.
X marks my nose I made.
So to perform features, clearly, we can't use the phrase preprocessing package available in the law
library, so the ascalon not preprocessed processing the package provides several common utility functions
and informal classes to modify the features available in our present representation that best our needs.
So.
We need to import our vacation.
So from Ashqelon Dautry processing.
In Paul.
I mean.
Mark Skoda.
So to scale up each features between a given minimum and maximum value, in our case between zero and
one, so that the maximum absolute value of each feature is scale to units I.
The main Muscala function can be used by using.
Skalla equa mean.
Mark.
Skalla.
So now, just to have confirmation of what we are doing, we bring the barometer that we use for the
next resizing.
So that bring.
Skalla Duffett.
I got the arrow, so.
Data is not defy.
So I fast forward because I take quite a bit of time.
So it is a bit tornadoes, hail and.
Now, we got an error here, so is actually data.
So now with that result, sort of it might hurt, can boost the minimum and maximum to be used for later
scaling.
So.
Now we can scale the features so that.
Make a note of caution here.
And we your data scale.
Iqua skalla don't fit on a train form, and in here I judge you the.
Data that we got.
Yet during this hail.
And there is no mistyped sort of fit to inform our fish that feed to data and to information on a mobile
array of chip is written so easily advisable to report the results in the starting form us the bandos
data frame, at least for comparison purposes.
So let's do it.
So is it got me.
July and then.
And in here, do you believe that?
Data from.
So.
Data scale and then column.
Equa.
P.
Named.
The.
No.
So let me run this hail and I got.
So what the name I think.
You should be shooting them and then we don't get any error, so to verify this defamation is carried.
We need to bring the basic statistics that we already calculated.
So let's bring this.
Statistics show summary Ucore.
Data scale, not this drive and then that brain.
Samori.
And think you and we got our results.
So how is one thousand twenty nine one thousand twenty nine one thousand twenty nine, so.
Now, every variable is included in the range between zero and one on features have value between zero
and one.
So as you say that they own him, but the value between zero and one.
And now we check the results by floating the variable box again, so as an astronaut box plot.
Data equal.
Data scale.
So this is our new book, Laws, and this Josh makes visual analysis much easier.
Now everything is clearer.
Is it possible to make a comparison between the predictors in what we can say, which features a rate
of variability in the data?
In addition, the possible outliners isolated pause.
I'm more highlighted and that is on this video.
I hope you enjoy it.
And I will see you in the next video.