Table 2.

Advantages and disadvantages of various ML algorithms [20–37].

Technique	Advantage	Disadvantage
SVM	It allows the generation of nonlinear boundaries	Choice of kernel
MLP	Ability to learn on its own	Complexity is high
DT	Independence of variables is not required. Easy to understand	High probability of overfitting
KNN	Training time is negligible	Does not work well with large dataset
NB	Training time remains constant irrespective of size	Features should be independent of each other