AI engineering glossary · Feature engineering data prep
What is backward elimination?
Backward elimination: A greedy wrapper method that starts with all features and removes, one at a time, the one whose removal hurts the model least, stopping when removal begins to hurt. It considers all features together at the start, but is costly with many features.
Backward elimination: an example
Fit a regression on all 30 predictors, drop the one with the highest p-value, refit and repeat until all remaining are significant.
Related terms
More Feature engineering data prep terms
- Bag of words
- Binary encoding
- Boruta
- Box-Cox transform
- Categorical variable
- Class imbalance
- Class weights
- Column transformer
Where you learn it
Backward elimination is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.