SupportVector

A support vector machine, or SVM, is a supervised machine learning algorithm used for classification and regression tasks. At its core, an SVM aims to find the best possible hyperplane that separates data points belonging to different classes in a high-dimensional space. This hyperplane is chosen such that the margin, the distance between the hyperplane and the nearest data points of any class, is maximized. These nearest data points are known as support vectors, and they play a crucial role in defining the decision boundary.

When dealing with data that is not linearly separable, SVMs employ a kernel trick. This technique implicitly maps the data into a higher-dimensional feature space where a linear separation might become possible. Common kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. The choice of kernel and its parameters significantly impacts the performance of the SVM. SVMs are effective in high-dimensional spaces and can handle situations where the number of dimensions exceeds the number of samples. They are known for their robustness and ability to generalize well, particularly when the data is noisy or contains outliers.