AI engineering glossary · Feature engineering data prep
What is feature transformation?
Feature transformation: Applying a mathematical function to an existing feature to change its scale or shape, such as a logarithm, square root or power. The aim is to make relationships closer to linear, reduce the influence of extreme values or satisfy a model's assumptions.
Feature transformation: an example
Monthly income in rupees ranges from 15,000 to 5,00,00,000; applying log10 compresses it to a range of about 4.2 to 7.7, so a linear model is not dominated by a few very high earners.
Related terms
More Feature engineering data prep terms
- Filter method
- Fit-transform pattern
- Forward selection
- Frequency encoding
- Geospatial features
- Group-aware splitting
- Hashing trick
- Hashing vectorizer
Where you learn it
Feature transformation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.