Hyperplane

Definition of Hyperplane

Hyperplane: A hyperplane is a mathematical concept used in linear algebra and machine learning. It is a subspace of a given vector space that is spanned by a set of vectors. In other words, it is a flat plane in a higher dimensional space. Hyperplanes can be used to separate different classes of objects in a data set, or to find the best fit line for a data set.

What is a Hyperplane used for?

A Hyperplane is a linear classifier used in machine learning, which uses a line or plane to separate two classes of data. It seeks to divide the data into two distinct clusters based on their attributes. A hyperplane is defined by a set of weights and bias values that define it in an n-dimensional space (where n is the number of features), providing a linear separation between two clusters. This hyperplane can be used for supervised learning tasks such as classification, where the goal is to assign labels or categories to data points, and regression problems, in which we are trying to determine the expected value of some outcome variable from one or more input variables. Generally speaking, hyperplanes are used when dealing with datasets that have many features and complex relationships between them. The goal is to find an optimal way to separate the classes of data while minimizing misclassification errors.