AI engineering glossary · Feature engineering data prep
What is feature extraction?
Feature extraction: Building a smaller or more useful set of new features from raw, often unstructured, data such as text, images or signals. Unlike selecting existing columns, it computes new representations, for example word counts from text or edge statistics from pictures. It reduces raw complexity into numbers a model can use.
Feature extraction: an example
From a customer-support email you extract word counts, email length and whether it contains a refund keyword, giving three numeric columns for a classifier.
Related terms
More Feature engineering data prep terms
- Feature scaling
- Feature selection
- Feature store
- Feature transformation
- Filter method
- Fit-transform pattern
- Forward selection
- Frequency encoding
Where you learn it
Feature extraction is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.