An assessment of ensemble learning approaches and single-based machine learning algorithms for the characterization of undersaturated oil viscosity

Beni-Suef University Journal of Basic and Applied Sciences

Table 4 Prediction performance of boosting techniques of ensemble method

Boosting regressors	\(R^2\)	MAE	MSE	RMSE	RMSLE
Gradient boosting	0.97881	0.006871	0.00011	0.01073	0.00875
XGBoosting	0.97864	0.00683	0.00011	0.01078	0.00879
AdaBoost-MLP	0.99943	0.00143	3.08633e\(-\)06	0.00175	0.00136
AdaBoost-SVM	\(-\)0.03092	0.07292	0.00561	0.07490	0.05894
AdaBoost-DT	0.97782	0.00704	0.00012	0.01098	0.00895
AdaBoost-LR	0.99930	0.00165	3.76101e\(-\)05	0.00193	0.00150