AI engineering glossary · Feature engineering data prep
What is mahalanobis distance?
Mahalanobis distance: A distance from a point to the centre of a distribution that accounts for the spread and correlation of the features, using the inverse covariance matrix: sqrt((x - mean)^T S^-1 (x - mean)). It is used to detect multivariate outliers.
Mahalanobis distance: an example
A person who is 150 cm tall and 90 kg may be unremarkable on each feature alone but far from the centre in Mahalanobis terms because the pair is unusual.
Related terms
More Feature engineering data prep terms
- Max-abs scaling
- Mean imputation
- Median imputation
- Min-max scaling
- Missing at random
- Missing completely at random
- Missing indicator
- Missing not at random
Where you learn it
Mahalanobis distance is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.