AI engineering glossary · Feature engineering data prep
What is standardization?
Standardization (also called Z-score scaling, StandardScaler): A scaling method that shifts a feature to mean 0 and stretches it to standard deviation 1: z = (x - mean) / std. Each value then says how many standard deviations it lies from the average. It suits algorithms that assume centred data, such as logistic regression, PCA and neural networks.
Standardization: an example
If the average delivery time is 30 minutes with a standard deviation of 8, a 46-minute delivery has z = (46 - 30) / 8 = 2.
Related terms
More Feature engineering data prep terms
- Stemming
- Stepwise selection
- Stop words
- Target encoding
- Text preprocessing
- Threshold tuning
- Tomek links
- Training-serving skew
Where you learn it
Standardization is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.