A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification tasks, particularly binary classification. It works by finding the optimal hyperplane that separates data points of different classes in a high-dimensional space, maximizing the margin between the closest points of each class, known as support vectors. SVMs are particularly effective for complex datasets and can handle both linear and non-linear classifications through the use of kernel functions.
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