Svm For Classification: How It Works And Why It’s Powerful

Support Vector Machines (SVMs) are a powerful and widely used machine learning algorithm for classification. SVMs are supervised learning algorithms that can be used for both classification and regression tasks. They are based on the concept of finding a hyperplane that best divides a dataset into two classes. The algorithm works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. SVMs are powerful because they can be used for both linear and non-linear classification tasks, are robust to noise and outliers, and are memory efficient.