AI engineering glossary · Feature engineering data prep
What is hashing vectorizer?
Hashing vectorizer (also called HashingVectorizer): A text featuriser that applies the hashing trick to word tokens, mapping them straight to a fixed number of columns without storing a vocabulary. It is memory-efficient and stateless, but the features cannot be traced back to words.
Hashing vectorizer: an example
HashingVectorizer(n_features=2**18) turns any text into a 262,144-column sparse vector without fitting a vocabulary first.
Related terms
More Feature engineering data prep terms
- Haversine distance
- High-cardinality feature
- Hot-deck imputation
- Information value
- Interaction feature
- Interpolation
- IQR method
- Iterative imputation
Where you learn it
Hashing vectorizer is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.