What is categorical embedding vs one-hot encoding?

Categorical embeddings convert categories into dense vectors, while one-hot encoding creates sparse binary vectors.

One-hot encoding is simple but inefficient for high-cardinality data. Embeddings learn semantic relationships between categories and reduce dimensionality significantly. They are trained as part of neural networks.