AI engineering glossary · Feature engineering data prep
What is threshold tuning?
Threshold tuning (also called Decision threshold): Choosing the probability cutoff at which a model's score is turned into a class label, instead of defaulting to 0.5. On imbalanced data, moving the threshold is a simple way to trade precision against recall.
Threshold tuning: an example
Lowering the fraud threshold from 0.5 to 0.2 catches more frauds (higher recall) but flags more genuine transactions (lower precision).
Related terms
More Feature engineering data prep terms
- Tomek links
- Training-serving skew
- Undersampling
- Unit vector scaling
- Unknown category handling
- Variance threshold
- Weight of evidence
- Window feature
Where you learn it
Threshold tuning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.