When should I use one-hot encoding in machine learning?

When should I use one-hot encoding in machine learning?

One-hot encoding is used when you have categorical variables that are not ordinal. It helps in converting these categories into a format that can be provided to ML algorithms.

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

Sure! If you have a feature like 'color' with categories like 'red', 'blue', and 'green', one-hot encoding will create three binary features: 'is_red', 'is_blue', and 'is_green'.

Are there any situations where I shouldn't use one-hot encoding?

Yes, if your categorical variable has a large number of categories, one-hot encoding can lead to a high-dimensional feature space, which may not be efficient.

What are some alternatives to one-hot encoding?

Alternatives include label encoding, binary encoding, or using embeddings, especially for high-cardinality categorical variables.