Welcome back.
In this video we're gonna talk again about standard scaler.
Because this is a very important concept you should learn in order to use it in the next and future
applications and model, because this is a crucial thing.
Standard scaler is a data pre-processing tool that transforms your features to have a mean of zero and
standard deviation of one.
This is also called standardization or zero or Z-score normalization.
Again, the formula equal z equal to x minus m over d x is the feature value minus the mean of the feature
all over the standard deviation of the feature.
Again, there is a very important note without standard scaler the weight feature.
For example, if we enter 3000 3000kg with completely dominate the cylinders feature, which is ranging
between 4 and 8 cylinders, making your neural network learning poorly.
So here we don't need to dominate by numbers.
We need to make a standard scalar in order to compare between the features.
So we have nine features every feature with different scale with different unit.
So we need to normalize all those features to one scale.
And this is the main purpose behind using standard scalar.
Don't worry we're going to see the standard scalar library in the next videos and lecture about it because
it's very important note and very important library and very important concept.
Not only importing it with the Sklearn.preprocessing.
It's a very important concept you should use in machine learning and in AI or anything when dealing
with different units and different features with different scales.
Standardscaler makes features compatible by giving them same scale critical for neural networks.
Help training converge, converge faster and preventing data leakage.
Fit on train.
Transform on test.
Simple concept.
Value minus mean over standard deviation.
This is the main concept behind it.
Without standard scaler, the weight feature 3000 or 4000kg would completely dominate the cylinder's
features or the, um displacement feature or the horsepower feature.
Making your neural network learn poorly.