Its the term given to the iterative process of building good features for a better model. Its the process that makes relevant features (using formulas and relations between others).
We use it when we have a refined and optimised model.
What does it involve
- Create new features from existing ones (e.g., ratios, interactions).
- Transform features to better capture non-linear relationships.
- Dimensionality Reduction if necessary.
The main techniques of feature engineering:
- are selection (picking subset),
- learning (picking the best),
- extraction and combination(combining).
Example: Predicting house prices. Raw features might be square footage, number of bedrooms, and location. Feature engineering could involve: Combining square footage and bedrooms into a “living space” feature.
Example:
- Decompose datetime information into separate features for date and time to capture their respective predictive powers.