What is Normalization?
Normalization is a process that transforms your data’s features to a standard scale, typically between 0 and 1. This is achieved by adjusting each feature’s values based on its minimum and maximum values. The goal is to ensure that no single feature dominates the others due to its magnitude.
Why Normalize?
- Improved Model Convergence: Algorithms like gradient descent often converge faster when features are on a similar scale.
- Fairness Across Features: In distance-based algorithms (e.g., k-nearest neighbors), normalization prevents features with larger ranges from disproportionately influencing results.
- Enhanced Interpretability: Comparing and interpreting feature importances is easier when they’re on the same scale.
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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