AI engineering glossary · Feature engineering data prep
What is feature engineering?
Feature engineering: The work of turning raw data into input columns that help a model learn, by cleaning, combining, transforming and creating variables. Good features expose the signal a model could not easily find on its own. For tabular data it is often the biggest driver of accuracy, more than the choice of algorithm.
Feature engineering: an example
From a raw timestamp of a Swiggy order, you create hour_of_day, is_weekend and is_lunch_rush columns, which predict delivery delay far better than the raw timestamp.
Related terms
- Feature extraction
- Feature selection
- Feature transformation
- Domain knowledge
More Feature engineering data prep terms
- Feature extraction
- Feature scaling
- Feature selection
- Feature store
- Feature transformation
- Filter method
- Fit-transform pattern
- Forward selection
Where you learn it
Feature engineering is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.