Support Vector Machines (SVMs) are a supervised learning algorithm that can be used for both classification and regression tasks. SVMs work by finding a hyperplane in the data that separates the classes as well as possible. The goal is to find a hyperplane that has the largest margin between the two classes, which will result in a more robust model. SVMs are often used for high-dimensional data where other algorithms may struggle.
SVM is a supervised learning algorithm.
The goal of SVM is to find a hyperplane that separates the data into two classes.
SVM is a linear classifier.