Prediction of Transverse Reinforcement of RC Columns Using Machine Learning Techniques

Table 3

Performance measure of the developed models.

Model	Training set				Testing set
	R^2	RMSE	MAE	WAPE	R^2	RMSE	MAE	WAPE
OLS	0.534	0.365	0.268	0.384	0.603	0.423	0.297	0.391
Lasso	0.533	0.366	0.267	0.384	0.595	0.427	0.300	0.395
Ridge	0.534	0.365	0.268	0.384	0.603	0.423	0.297	0.391
KNN	1.000	<0.001	<0.001	<0.001	0.775	0.318	0.194	0.256
SVR	0.941	0.129	0.075	0.108	0.733	0.347	0.233	0.307
MLP	0.738	0.274	0.191	0.274	0.719	0.356	0.233	0.308
DT	1.000	5.736	7.671	1.100	0.772	0.32	0.182	0.241
RF	0.951	0.118	0.079	0.114	0.838	0.27	0.185	0.244
AdaBoost	0.638	0.322	0.280	0.402	0.717	0.356	0.299	0.395
XGBoost	0.999	0.006	0.004	0.006	0.873	0.239	0.161	0.212
LightGBM	0.977	0.08	0.053	0.076	0.817	0.286	0.192	0.254
CatBoost	0.978	0.078	0.057	0.082	0.842	0.266	0.179	0.237