What are some alternatives to one-hot encoding in machine learning?

What are some alternatives to one-hot encoding in machine learning?

Some alternatives include label encoding, binary encoding, and target encoding. Each has its own use cases.

Can you explain label encoding and when to use it?

Label encoding assigns a unique integer to each category. It's useful when the categorical variable has an ordinal relationship.

What about binary encoding? How does it work?

Binary encoding converts categories into binary code. It's more memory efficient than one-hot encoding, especially with high cardinality.

Are there any downsides to using these alternatives?

Yes, label encoding can introduce ordinal relationships that don't exist, and binary encoding can be complex to interpret.