Factors to Consider When Choosing Normalization
- Effect on sparsity: Normalization may alter the sparsity of the data, particularly if the original features contain many zero values.
- Robustness: It’s sensitive to outliers, especially if the range of values is skewed by extreme values.
- Feature importance: Normalization can potentially distort the importance of features if the range of values is not representative of their significance.
- Impact on distance-based algorithms: It can affect the performance of distance-based algorithms if the distance metric relies on the scale of features.
- Handling categorical features: Normalization needs special treatment for categorical features, as they don’t have meaningful minimum and maximum values.
- Impact on interpretability: It retains the original interpretation of the data within a known range, making it easier to interpret feature values.
- Computational efficiency: Normalization can be computationally expensive for large datasets, as it requires finding minimum and maximum values for each feature.
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