# The Support Vector Machine Algorithm

The **Support Vector Machine** (**SVM**) algorithm is a classifier that finds the hyperplane that effectively separates the observations into their class labels. It starts by positioning each instance into a data space with *n* dimensions, where *n* represents the number of features. Next, it traces an imaginary line that clearly separates the instances belonging to a class label from the instances belonging to others.

A support vector refers to the coordinates of a given instance. According to this, the support vector machine is the boundary that effectively segregates the different support vectors in a data space.

For a two-dimensional data space, the hyperplane is a line that splits the data space into two sections, each one representing a class label.

## How Does the SVM Algorithm Work?

The following diagram shows a simple example of an SVM model. Both the triangles and circular data points represent the instances from the input dataset, where...