. 2021 Jun 24;2(7):100291. doi: 10.1016/j.patter.2021.100291

Table 3.

Machine learning regression algorithms employed in this work

Machine learning algorithm	Abbreviation
Extremely randomized trees⁷⁶^,⁸³^,¹⁰³^,¹⁰⁴	ERT
Boosted decision trees⁷⁶^,⁹²^,102, 103, 104	BDT
Bagging with decision trees⁷⁶^,⁹⁰,⁹³^,¹⁰³^,¹⁰⁴	B/DT
Random forest⁷⁶^,⁹⁰^,⁹⁴^,¹⁰³^,¹⁰⁴	RF
Bagging with random forest⁷⁶^,⁹³^,⁹⁴^,¹⁰³^,¹⁰⁴	B/RF
Gradient boosting⁷⁶^,⁹²^,⁹⁵^,102, 103, 104	GB
Decision trees⁷⁶^,⁹⁰^,¹⁰³,¹⁰⁴	DT
Nu-support vector machine with radial basis function (RBF) kernel⁷⁶^,⁷⁹^,⁹⁰^,⁹⁶^,⁹⁸^,¹⁰³,¹⁰⁴	Nu-SVM/RBF-K
Support vector machine RBF kernel⁷⁶^,⁷⁹^,⁹⁰^,⁹⁷^,⁹⁸^,¹⁰³,¹⁰⁴	SVM/RBF-K
Support vector machine with linear kernel⁷⁶^,⁷⁹^,⁹⁶^,⁹⁹^,¹⁰³,¹⁰⁴	SVM/L-K
Linear regression76, 77, 78^,⁹⁹^,¹⁰⁰^,¹⁰³,¹⁰⁴	LR
Ridge regression76, 77, 78^,⁹⁹^,¹⁰⁰^,¹⁰³,¹⁰⁴	RR
K-nearest neighbors⁷⁶^,⁹⁰^,¹⁰¹^,¹⁰³,¹⁰⁴	K-NN
AdaBoost⁷⁶^,⁹²^,102, 103, 104	AB