Choosing Between Normalization and Scaling
Normalization and scaling are both techniques used to preprocess numerical data before feeding it into machine learning algorithms.
| Criteria | Normalization | Scaling |
|---|---|---|
| Purpose | Adjusts values to fit within a specific range, typically between 0 and 1. | Adjusts values to have a mean of 0 and a standard deviation of 1, without necessarily constraining them to a specific range. |
| Range of values | Transforms data to a common scale, preserving the shape of the original distribution. | Centers the data around 0 and scales it based on the standard deviation. |
| Effect on outliers | Can be sensitive to outliers since it uses the minimum and maximum values. | Less sensitive to outliers since it calculates based on mean and standard deviation. |
| Algorithm compatibility | Often used with algorithms that rely on distance measures, like KNN or SVM. | Suitable for algorithms that assume zero-centered data, like PCA or gradient descent-based optimization. |
| Computation | Requires finding the minimum and maximum values for each feature, which can be computationally expensive for large datasets. | Involves calculating the mean and standard deviation of each feature, which is computationally efficient. |
| Distribution preservation | Preserves the shape of the original distribution, maintaining the relative relationships between data points. | May alter the distribution slightly, particularly if the data has a non-Gaussian distribution. |
| Data type suitability | Suitable for features with a bounded range or when the absolute values of features are meaningful. | Suitable for features with unbounded ranges or when the mean and variance of features are meaningful. |
| When to use | When the scale of features varies significantly, and you want to bring them to a comparable range. Particularly useful when the algorithm doesn’t make assumptions about the distribution of the data. | When features have different units or scales and you want to standardize them so that each feature contributes equally to the analysis. It’s also useful when algorithms assume that features are centered around zero. |
If the data does not follow a Gaussian distribution or if you are unsure, normalizing your data is a good idea. One exception to the above could be when your data is normally distributed. Scaling is necessary when the algorithm you are using, such as support vector machines (SVM) and linear regression, requires that your data be normally distributed or in some cases a Gaussian Distribution.
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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