When should I use normalization in data processing?

When should I use normalization in data processing?

Normalization is used when you want to scale your data to a specific range, often between 0 and 1.

Why is normalization important for machine learning?

Normalization helps improve the performance of machine learning algorithms by ensuring that features contribute equally.

Can you give me an example of when to normalize data?

Sure! If you're working with features like age and income, normalizing ensures that neither dominates the model.

Are there any situations where I shouldn't normalize?

Yes, if your data is already on a similar scale or if you're using tree-based algorithms, normalization may not be necessary.