AI engineering glossary · Feature engineering data prep
What is min-max scaling?
Min-max scaling (also called Normalisation to [0,1], MinMaxScaler): A scaling method that linearly maps a feature to a fixed range, usually 0 to 1, using the smallest and largest values seen in training data: x_scaled = (x - min) / (max - min). It preserves the shape of the distribution but is sensitive to extreme values, which squash the rest into a narrow band.
Min-max scaling: an example
Marks out of 100 in a class range from 40 to 90. A mark of 65 becomes (65 - 40) / (90 - 40) = 0.5.
Related terms
More Feature engineering data prep terms
- Missing at random
- Missing completely at random
- Missing indicator
- Missing not at random
- Missing value imputation
- Mode imputation
- Multiple imputation
- Mutual information
Where you learn it
Min-max scaling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.