One of the most popular supervised learning algorithms is the Support Vector Machine, or SVM for short. It may be used to both regression and classification problems. However, in Machine Learning, it is most often used to Classification problems.

The **Support Vector Machines** method is used to find the best possible line (or decision boundary) to use in categorising data in n-dimensional space. In the future, we’ll be able to easily classify any more data points using this method. This ideal decision boundary is often represented as a hyperplane.

SVM is used to pick out the hyperplane’s most extreme points and vectors. The name “Support Vector Machine” comes from the term “support vector,” which is used to describe these out-of-the-ordinary occurrences. Below is an illustration of how a decision boundary or hyperplane may be used to separate two groups of people from one another.

**Support Vector Machine Algorithm**

Example: It is feasible to understand SVM by using an example similar to what we used for the KNN classifier. Using the SVM technique, we may construct a model that can successfully distinguish between cats and dogs, in the event that we encounter atypical cats that also display features typical of dogs. Our model will be put through its paces utilising this oddball as a test subject after being exposed to several cat and dog examples for it to get acquainted with the various defining qualities of these animals. Thus, the support vector will see the cat and the dog in their most extreme forms because it builds a judgement boundary between the two sets of data (the cat and the dog) and selects extreme instances (support vectors). Using the specified support vectors, the object will be correctly classified as a cat. Please review the diagram that is shown below.

**Support Vector Machine Algorithm**

Face recognition, picture and text classification, and related tasks are all possible applications of the support vector machine (SVM) method.

**Two types of SVMs exist, and they are as follows:**

**Linear support vector machine**

The linear support vector machine (SVM) is a classifier used for linearly separable data. Therefore, a Linear SVM classifier is employed for data that can be neatly separated into two categories by a single line, and the corresponding dataset is said to have linearly separable data.

**An alternative to linear SVM**

Data that defies neat categorization along a straight line is said to be non-linear, and a non-linear support vector machine (SVM) classifier is used to make sense of it. For non-linearly disjoint data, a non-linear support vector machine is utilised.

**When using the SVM technique, the hyperplane and support vectors look like this**

Hyperplane: It is our responsibility to choose the line or boundary in n-dimensional space that best assists in categorising the data points, given that there may be several lines or decision boundaries that help to divide the classes. The hyperplane is the term used by the SVM community to describe this ideal border.

**Conclusion**

The hyperplane’s dimensions are specified by the total number of features in the dataset. This indicates that the hyperplane will take the shape of a straight line if there are just two features (as indicated in the illustration). Moreover, if there are three features, the hyperplane will be a two-dimensional plane.

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