Another widely used supervised machine learning algorithm is support vector machines. Like in logistic regression, we tried to fit a curve that passes through the data points, but in SVMs we try to find hyperplanes that divide the given data into regions, with each region representing a particular label.
What are hyperplanes? They are nothing but a generalization of a plane. For example, in one-dimension, it is a point, in two-dimensions, it is a line, in three-dimensions, it is a plane, and for even higher dimensions, we just call them hyperplanes.
The following diagram provides a visual of how linear SVMs work: