SVMs are the most preferred and favorite machine learning algorithms by many data scientists due to their accuracy with less computation power. They are employed for both regression and classification problems. They also offer a kernel trick to model non-linear relationships. SVM has a variety of use cases, such as intrusion detection, text classification, face detection, and handwriting recognition.
SVM is a discriminative model that generates optimal hyperplanes with a large margin in n-dimensional space to separate data points. The basic idea is to discover the Maximum Marginal Hyperplane (MMH) that perfectly separates data into given classes. The maximum margin means the maximum distance between data points of both classes.
Terminology
We will now explore some of the terminology that goes into SVM classification:
- Hyperplane: Hyperplane is a decision boundary used to distinguish between two classes. Hyperplane dimensionality is decided by the number of features...
