Machine learning model for the optimization and kinetics of petroleum industry effluent treatment using aluminum sulfate

Journal of Engineering and Applied Science

Table 3 ML model statistical evaluations using fivefold cross-validation

fivefold validation metrics
ML model	MAE	RMSE	pred-R²	AAD	Std. dev
Random forest regression (RF)	26.21	40.92	0.90	27.10	40.97
Decision tree regression (DT)	26.23	41.15	0.81	29.10	41.11
Support vector machine (SVM)	55.10	77.37	0.33	72.29	77.42
Polynomial regression (PM)	44.68	60.89	0.58	50.97	60.92