Factors to Consider When Choosing Scaling
- Effect on sparsity: Scaling generally preserves sparsity, especially if the scaling factor does not affect zero values.
- Robustness: Scaling is more robust to outliers due to centering data around the mean and scaling based on standard deviation.
- Feature importance: It preserves the relative importance of features, as it only adjusts their scale and center.
- Impact on distance-based algorithms: Scaling is less likely to affect the performance of distance-based algorithms, as the scale is standardized across features.
- Handling categorical features: Scaling treats categorical features similarly to numerical features, which may not always be appropriate.
- Impact on interpretability: Scaling may slightly affect interpretability, especially if the scale and center of features are transformed.
- Computational efficiency: Scaling is more computationally efficient compared to normalization, as it involves calculating mean and standard deviation 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
Contact Us