AI engineering glossary · Classical ML algorithms
What is manhattan distance?
Manhattan distance: The distance between two points measured by summing the absolute differences of their coordinates, like walking along city blocks. It is less affected by a single large difference than straight-line distance and is used in neighbour and clustering methods.
Manhattan distance: an example
Between (1, 2) and (4, 6) it is |4-1| + |6-2| = 7.
Related terms
- Euclidean distance
- k-nearest neighbours
- Minkowski distance
More Classical ML algorithms terms
- Matrix factorisation
- Maximum margin classifier
- Mini-batch k-means
- Multicollinearity
- Multinomial naive Bayes
- Multiple linear regression
- Naive Bayes
- Non-negative matrix factorisation
Where you learn it
Manhattan 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.