What is the difference between flattening and normalization in data processing?

What is the difference between flattening and normalization in data processing?

Flattening is the process of converting a multi-dimensional structure into a single dimension, while normalization adjusts values to a common scale.

Can you give me an example of when to use flattening?

Sure! Flattening is often used when preparing data for machine learning models that require a single input vector.

And what about normalization? When should I use that?

Normalization is useful when you want to ensure that different features contribute equally to the distance calculations in algorithms.

Are there any downsides to flattening or normalization?

Yes, flattening can lead to loss of structure, while normalization can distort relationships if not applied carefully.