AI engineering glossary · Feature engineering data prep
What is entity embedding?
Entity embedding: Learning a dense, low-dimensional vector for each category value as part of training a neural network, so similar categories end up with nearby vectors. It is an alternative to one-hot encoding for high-cardinality categories and can be reused by other models.
Entity embedding: an example
A retail model learns a 10-number vector for each of 5,000 store IDs, and stores with similar demand patterns end up with similar vectors.
Related terms
More Feature engineering data prep terms
- Equal-frequency binning
- Equal-width binning
- Feature crossing
- Feature engineering
- Feature extraction
- Feature scaling
- Feature selection
- Feature store
Where you learn it
Entity embedding is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.