What is Scaling?
Scaling is a broader term that encompasses both normalization and standardization. While normalization aims for a specific range (0-1), scaling adjusts the spread or variability of your data.
Why Scale?
- Robustness to Outliers: Scaling can make your models less sensitive to extreme values.
- Algorithm Compatibility: Some algorithms, like Support Vector Machines and Principal Component Analysis, work best with scaled data.
Normalization and Scaling
Normalization and Scaling are two fundamental preprocessing techniques when you perform data analysis and machine learning. They are useful when you want to rescale, standardize or normalize the features (values) through distribution and scaling of existing data that make your machine learning models have better performance and accuracy.
This guide covers the following strategies and explains their importance, varied approaches, as well as real-world examples.
Table of Content
- What is Normalization?
- Types of Normalization Techniques
- What is Scaling?
- Different types of Scaling Techniques
- Choosing Between Normalization and Scaling
- Importance of Normalization and Scaling
- Factors to Consider When Choosing Normalization
- Factors to Consider When Choosing Scaling
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