Feature Scaling | Data Normalization vs Data Standardization
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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