Support Vector Machines (SVMs) are a set of supervised learning algorithms primarily used for classification tasks, although they can also be applied to regression and outlier detection. The core idea behind SVMs is to find an optimal hyperplane that separates different classes in the data. This hyperplane is chosen to maximize the margin, which is the distance between the hyperplane and the nearest data points from each class, known as support vectors. This characteristic makes SVMs particularly effective for high-dimensional datasets and complex data structures, promoting good generalization and reducing the risk of overfitting ³⁵.

SVMs offer several advantages, including their effectiveness in high-dimensional spaces and their memory efficiency, as they only use a subset of training points (support vectors) in the decision function. They are versatile, allowing for different kernel functions to be specified for the decision boundary, which can be linear or non-linear depending on the dataset ⁵. However, SVMs can be sensitive to the choice of kernel and regularization parameters, and they do not directly provide probability estimates without additional computation ⁵.