AI engineering glossary · ML fundamentals
What is linear separability?
Linear separability (also called Linearly separable): A property of a labelled dataset meaning that a single straight line, or flat plane in higher dimensions, can perfectly split the classes. Real data rarely satisfies it, which is why flexible boundaries or feature transformations are often needed.
Linear separability: an example
Points of two classes on either side of the line x + y = 5 are separable; two classes arranged in concentric rings are not.
Related terms
More ML fundamentals terms
- Machine learning
- Majority-class baseline
- Manifold hypothesis
- Meta-learning
- Model
- Model capacity
- Model selection
- Model variance
Where you learn it
Linear separability is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.