Support Vector Machine

A support vector machine (SVM) is used to find the optimal dividing lines between classes of training data within a vector space.

Support vector machines are employed in classification tasks where the goal is to separate different classes within a dataset. They work by identifying the hyperplane that best divides the classes in the feature space. In cases where classes are not linearly separable, SVMs use kernel functions to map input vectors to higher-dimensional spaces where a linear separation is possible.

For example, given a dataset of points belonging to two classes, an SVM will find the line (or hyperplane in higher dimensions) that maximizes the margin between the classes. This margin is defined by the support vectors, which are the data points closest to the hyperplane.

Support vector machines are important because they provide a robust method for classification, especially in high-dimensional spaces. They are also versatile, handling both linear and non-linear classification tasks effectively.