Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Table 2

Accuracy statistics of ML regression models using training and testing datasets.

Model	Training dataset			Testing dataset
	RMSE	MAE		RMSE	MAE
XGB	0.0076	0.0063	0.9999	0.4033	0.2845	0.7227
CAT	0.1594	0.1224	0.9565	0.4704	0.3503	0.6229
GBT	0.0018	0.0015	0.9995	0.4812	0.3440	0.5981
MLP-NN	0.3249	0.2663	0.8194	0.4829	0.41115	0.6025
SVR	0.3964	0.3249	0.7313	0.5032	0.4334	0.5684
DTR	0.1056	0.0720	0.9809	0.5712	0.3537	0.4927

Notes: root mean square error, RMSE; mean absolute error, MAE; coefficient of determination, R²; multilayer perceptron neural network, MLP-NN; Support Vector Regression, SVR; Cat Boost Regression, CAT; Decision Tree Regression, DTR; Gradient Boosting Regression, GBR; Extreme Gradient Boosting Regression, XGB.