Support Vector Machines (SVMs) are supervised machine learning models that are
widely used for classification and regression tasks. SVMs are particularly effective in dealing
with high-dimensional and complex datasets. The key idea behind SVMs is to find an optimal
hyperplane that separates the data points of different classes with the maximum margin. The
hyperplane is defined as the decision boundary that maximizes the distance between the
closest data points of different classes, called support vectors. SVMs aim to achieve both good
classification performance and robustness to new data.