Margin (machine learning)

hideThis article has multiple issues. Please help or discuss these issues on the talk page. (Learn how and when to remove these template messages) This article may be too technical for most readers to understand. Please to make it understandable to non-experts, without removing the technical details. (September 2010) (Learn how and when to remove this template message) This article does not cite any sources. Please help by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources:  –  ·  ·  · scholar · JSTOR (August 2014) (Learn how and when to remove this template message) (Learn how and when to remove this template message)

H₁ does not separate the classes.
H₂ does, but only with a small margin.
H₃ separates them with the maximum margin.

In machine learning the margin of a single data point is defined to be the distance from the data point to a decision boundary. Note that there are many distances and decision boundaries that may be appropriate for certain datasets and goals. A margin classifier is a classifier that explicitly utilizes the margin of each example while learning a classifier. There are theoretical justifications (based on the VC dimension) as to why maximizing the margin (under some suitable constraints) may be beneficial for machine learning and statistical inferences algorithms.

There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So we choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier; or equivalently, the perceptron of optimal stability.^{[citation needed]}

This computer-programming-related article is a stub. You can help Wikipedia by .

• v
• t