Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

Table 3

The performance of all the regression models.

	Metrics
Models	MAE	MSE	RMSE	R^2	RRMSE
DT	6.08	129.58	11.38	54.8936	0.19
SVM	8.55	118.90	10.90	58.6104	0.20
LR	8.13	86.28	9.29	69.9663	0.17
KNN	4.59	38.36	6.19	86.6482	0.10
LGBM	4.87	38.32	6.18	86.6942	0.11
XGB	4.56	34.84	5.90	87.8705	0.10
RF	3.60	24.10	4.91	91.6111	0.09
ETR	3.46	22.83	4.78	92.0511	0.08
BagXGB	3.62	22.44	4.74	92.1885	0.08
BagETR	3.76	24.00	4.90	91.6450	0.08
Voting	3.42	21.74	4.66	92.4307	0.08