In Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions by thinking of one set of points as being colored blue and the other set of points as being colored red.

How do you know if data is linearly separable?

Clustering method: If one can find two clusters with cluster purity of 100% using some clustering methods such as k-means, then the data is linearly separable.

What does linearly separable data mean?

A dataset is said to be linearly separable if it is possible to draw a line that can separate the red and green points from each other. In n dimensions, the separator is a (n-1) dimensional hyperplane - although it is pretty much impossible to visualize for 4 or more dimensions.