What Is Support Vector Machine Interpret Method For Support Vector Machine

The support vector machine is a machine learning algorithm that follows the supervised learning paradigm and can be used for both classifications as well as regression problems though this is primarily a classification algorithm. This algorithm primarily works by designing the hyperplanes and increasing the margin between them.

Introduction Support Vector Machine
In the domain of machine learning, support vector machines belong to the associated learning under the broader domain of supervised learning. These models can be used for classification as well as regression. This algorithm particular belongs to the class of non-probabilistic binary classifiers though under few variations like Platt scaling and allied stuff that these can also be used probabilistic classifiers. Being a binary classifier, SVM owing to its inherent design can only assign the data points to two classes only. A model utilizing SVM maps the data points in space in such a fashion that two classes are separated by a clear gap and are concerned about increasing the gap.

In support vector machines that utilize the n-features for its input, the data items are plotted as a point in N-Dimension space with each feature corresponding to the value of a particular coordinate. Then, we check the class to which it belongs. By the basic definition of SVM, it’s a binary classifier and thus, utilizes the normal XY-coordinate geometry which is depicted in figure 1, below.

Defining a support vector:
The data points that are close to the hyperplane and have an impact on the position and alignment of the hyperplane are called support vectors. These data points this algorithm to its name as well. The main aim of using a support vector is to increase the functional margin which is introduced with the hyperplane. Changing or removing these datapoints alter the position and orientation of hyperplane as well.

Hyperplane:
The decision boundaries that help classify the data are called the hyperplanes. Data points that fall on either side of the hyperplane belong to the same class. The number of dimensions to which a hyperplane exists also varies according to the number of features in the input space. If there are only two features in the input space, then the hyperplane is a straight line as shown in Fig. 1. Whereas, if there 3 features then hyperplane is a dimensional plane. For dimensional greater then 3D, it will also exit but the visualization is not possible. The hyperplane generated ios separated from the support vectors with some margin. This margin should be as large as possible. Figure 2 depicts the concept of margin which is technically known as the functional margin and it remains an important concept in support vector machines.

> Read: 39+ Statistics Interview Question and Answers

Cost Function:
The functioning of the support vector machines is all about increasing the margin between the data points and the hyperplane. A loss function named as hinge loss is one of the most commonly used loss function to perform this activity. Mathematically, Hinge loss is given by:

c(x,y,f(x)= f(x)=(