2.1.1. Support Vector Machines (SVMs)

The support vector machine algorithm is a popular supervised algorithm used both in classification and regression models [40]. In classification, the SVM aims to identify a hyperplane in an N-dimensional feature space, which effectively separates the data points into distinct classes (Figure 1A), while, in regression models, the SVM aims to find a line that best fits the data [41]. The kernel-based SVM algorithm uses kernel functions to transform the input data into a higher dimensional feature space when the data cannot be separated linearly. The performance of the SVM model depends on two hyperparameters: kernel parameters and kernel types. The selection of the kernel type is determined based on the characteristics of the input data [29].
