An SVM plane is a linear hyperplane in a high-dimensional space that best separates the different classes. It is formed by finding the plane that maximizes the...
An SVM plane is a linear hyperplane in a high-dimensional space that best separates the different classes. It is formed by finding the plane that maximizes the margin between the classes, while minimizing the distance between the points belonging to different classes.
In other words, the goal is to find a hyperplane that best separates the classes while leaving as much distance between them as possible.
The equation of a plane in a high-dimensional space can be written as:
where:
a, b, and c are the coefficients of the variables in the equation
d is the constant term
The margin of a hyperplane is the distance from the hyperplane to the closest point on the margin. The margin of an SVM plane is the distance from the hyperplane to the closest point on either class.
SVM planes are powerful tools for classification, as they can be used to achieve high accuracy with minimal misclassification. They have been widely used in various applications, including medical diagnosis, fraud detection, and image recognition