Feature Scaling | Data Normalization vs Data Standardization смотреть онлайн

In this video, you will learn

What is Data Normalization?
What is Feature Scaling?
What are the different types of feature scaling?
What is Min-Max Normalization?
What is Z-Score Normalization?
What is Data Standardization?
What is Robust Scaling?


Feature Scaling

Feature scaling is an essential step in data preprocessing for machine learning algorithms. It is a technique used to transform the range of independent variables or features of data to a standard scale. Feature scaling is also known as data normalization or standardization.


Min-Max Normalization
Each attribute is scaled to a range between [0,1]. This can be achieved using Scikit-Learn’s MinMaxScaler. The transformation is given by

X_scaled = (X - X_min) / (X_max - X_min)



Z-Score Implementation
Scikit-learn provides us with StandardScaler which standardizes the features for us.
The z-score of a sample value x is given by:
z=(x - u)/s.


Timestamps:

00:05 - Feature Scaling
04:10 - Min-max Normalization
08:10 - Z-Score Normalization
11:00 - Robust Scaling

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