When should I use min-max scaling in my data preprocessing?

When should I use min-max scaling in my data preprocessing?

Min-max scaling is useful when you want to normalize your data to a specific range, typically [0, 1]. It helps in algorithms that are sensitive to the scale of data.

Can you give me an example of when it's particularly beneficial?

Sure! It's particularly beneficial for algorithms like k-nearest neighbors or neural networks, where distance calculations are important.

Are there any situations where I shouldn't use it?

Yes, if your data contains outliers, min-max scaling can distort the data distribution. In such cases, consider using robust scaling instead.

What are some alternatives to min-max scaling?

Alternatives include standardization (z-score normalization) and robust scaling, which are better for handling outliers.