Table 9 Comparative evaluation of performance using GUI.

From: Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis

Algorithms

CS (MPa)

R2

RMSE (MPa)

MAE (MPa)

MAPE (%)

AdaBoost

50.99

0.744

6.90

5.74

15.92

XGBoost

50.22

0.950

3.06

1.41

4.53

Gradient Boosting

51.25

0.903

4.25

3.08

9.00

CatBoost

50.49

0.942

3.28

1.94

6.02

Decision Tree

46.82

0.924

3.75

1.56

4.94

LightGBM

51.83

0.914

3.98

2.71

8.07

Experimental

48.17

Lower and upper bound of 46.88 MPa and 49.46 MPa

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