$1$: SVM $-$ In the early $2000$s$,$ the support vector machine $($SVM$)$ model class was the most popular approach for $$off$-$the$-$shelf$$ supervised learning, for the cases when no specialized prior knowledge about a domain is known. An SVM aims at devising a computationally efficient way of segregating different data classes with a hyper$-$plane that best differentiates the classes. SVM selects this hyper$-$plane $($i$.$e$.,$ the maximum margin separator$)$ from the hypothesis space that maximizes the distance to data points of either class. The support vectors are the data points that are closest to the separator constructed by the SVM model $($three circled points in Figure below$).$