Table 2.

Machine learning algorithms and their main features.

ML Algorithm	Basic Idea	Features
NB	Probabilistic classifier	Cannot handle missing data, stable performance [25].
SVM	Hyperplane optimization	Highly accurate models, less likely to suffer from overfitting, used for prediction and classification tasks.
DT	tree-structured model	Robust, for categorical data, easy to interpret.
RF	DT ensemble method	Effective for highly complex problems, best for high-dimensional data sets, can handle missing data and imbalanced data sets.
AdaBoost	Ensemble algorithm	Improves the performance of individual weak classifiers, sensitive to noise.
KNN	Based on a distance metric to measure the distance between data points.	Choice of a distance metric affects performance; known as lazy learner, as it does not perform any analysis until it is presented with a testing data point.
GBDT	Ensemble tree induction, seeks to produce a model that minimizes the loss function	Highly flexible [31].
LR	Predicts the probability that a given data point belongs to a certain class	Easy calculation, can handle continuous numerical values, cannot handle non-linear data.
ANN	Inspired by networks of biological neurons	Highly accurate models, difficult to interpret the model (black-box models), requires a large number of parameters.
ET	Ensemble tree induction	Good performance, easy to implement, less computational time, fewer optimization parameters [32].