*Basic

The idea of linear separability in AI is to check if you can separate points in an n-dimensional space using only n-1 dimensions.[1]

**Intermediate

Linearly separable data is data that if graphed in two dimensions, can be separated by a straight line.[2] In the image below[3], the line from upper left to lower right separates the red and blue classes.

In two dimensional space, linear separability means there is a line, which separates points of one class from points of another class.[4] So linear separability implies that if there are two classes then there will be a point, line, plane or hyperplane that splits the input features in such a way that all points of one class are in one half space and the second class is in the other half-space.[5]

***Advanced

If the data is not linear-separable, a kernel function is used.[6]

Sources

[1] Editors (2022). Linearly separable data in neural networks. Baeldung.com
[2] Editors. Machine learning: Linear Separability. Engineering.purdue.edu
[3] Editors. Linear Separability. Wikipedia. en.wikipedia.org
[4] Editors (2019). How to check for linear separability. Maurygreen.medium.com
[5] Editors. Neural networks: What does “linearly separable” mean? Intellipaat.com
[6] Editors. Linear separability. Aishack.in