What is the difference between scaling and normalization?

Scaling and normalization are both techniques used in data pre-processing to transform data and make it suitable for analysis, but they have different goals and methods.

 

Scaling is a process that involves transforming data to a specific range or scale. This is typically done to ensure that all features of the data are on a similar scale and have equal importance in the analysis. For example, in machine learning algorithms that use distance measures, scaling is often used to ensure that features with large values do not dominate the analysis. Scaling techniques include Min-Max scaling, Z-score scaling, and Log scaling.

 

Normalization, on the other hand, is a process that involves transforming data to a common scale while preserving the shape of its distribution. This is typically done to remove the effect of scale and to make the data more interpretable. Normalization techniques include simple feature scaling, decimal scaling, and Z-score normalization.

 

In summary, scaling and normalization are both data transformation techniques used in data pre-processing, but they have different goals and methods. Scaling is used to ensure that all features of the data have equal importance in the analysis, while normalization is used to remove the effect of scale and make the data more interpretable.