Linear separability is a problem in machine learning that refers to the ability of a set of data points to be separated into distinct categories using a linear decision boundary. If a set of data points can be separated by a straight line, then it is said to be linearly separable. However, not all sets of data points are linearly separable, and there are a number of ways to deal with this problem.