During training, the algorithm adjusts its parameters to minimize the discrepancy between predicted and actual outputs. Supervised learning is widely used in tasks such as classification and regression. Examples of supervised learning models include naïve Bayes (NB) (Gomez-Alvarez and Revetta, 2020), logistic regression (LR) , support vector machines (SVM) , k-nearest neighbor (KNN) , decision trees (DT) , random forests (RF) , and extreme gradient boosting (XGB) .