Table 5.

Regression models comparison by evaluation metrics (MSE, RMSE, MAE, and R²) before and after reduction.

			Linear Regression						AdaBoost
			Ridge Regression		Simple linear Regularization		Elastic Net Regression
			Before feature reduce.	After feature reduce.	Before feature reduce	After feature reduce	Before feature reduce	After feature reduce	Before feature reduction	After feature reduce
Linear Regression	Ridge Regression	MSE			0.147	0.020	0.931	0.918	0.758	0.502
		RMSE			0.135	0.020	0.960	0.942	0.890	0.853
		MAE			0.110	0.013	0.986	0.974	0.998	0.994
		R2			0.956	0.854	0.045	0.089	0.110	0.162
	Simple Linear Regularization	MSE	0.853	0.980			0.930	0.918	0.758	0.502
		RMSE	0.865	0.980			0.960	0.942	0.890	0.853
		MAE	0.890	0.987			0.986	0.974	0.998	0.994
		R2	0.044	0.146			0.045	0.089	0.110	0.162
	Elastic Net Regression	MSE	0.069	0.082	0.070	0.082			0.071	0.291
		RMSE	0.040	0.058	0.040	0.058			0.313	0.724
		MAE	0.014	0.026	0.014	0.026			0.949	0.980
		R2	0.955	0.911	0.955	0.911			0.228	0.213
AdaBoost		MSE	0.242	0.498	0.242	0.498	0.929	0.709
		RMSE	0.110	0.147	0.110	0.147	0.687	0.276
		MAE	0.002	0.006	0.002	0.006	0.051	0.020
		R2	0.890	0.838	0.890	0.838	0.772	0.787