Modeling nanofluid viscosity: comparing models and optimizing feature selection—a novel approach

Onyiriuka, Ekene

doi:10.1186/s42269-023-01114-w

Bulletin of the National Research Centre

Table 2 Models performance and comparison

From: Modeling nanofluid viscosity: comparing models and optimizing feature selection—a novel approach

Model type	Preset	RMSE (validation)	MSE (validation)	R² (validation)	MAE (validation)	MAE (test)	MSE (test)	RMSE (Test)	R² (test)
Gaussian process regression	Custom Gaussian process regression	0.022	0.0005	0.9941	0.0146	0.0157	0.0004	0.0211	0.9924
Gaussian process regression	Custom Gaussian process regression	0.0228	0.0005	0.9936	0.0129	0.0186	0.0006	0.0251	0.9894
Gaussian process regression	Custom Gaussian process regression	0.0232	0.0005	0.9934	0.0131	0.0186	0.0006	0.0251	0.9894
Gaussian process regression	Custom Gaussian process regression	0.0263	0.0007	0.9915	0.0144	0.0237	0.0012	0.034	0.9805
Gaussian process regression	Exponential GPR	0.0302	0.0009	0.9889	0.0172	0.0201	0.0008	0.0278	0.987
Gaussian process regression	Rational quadratic GPR	0.0341	0.0012	0.9858	0.0192	0.0203	0.0008	0.0283	0.9864
Gaussian process regression	Matern 5/2 GPR	0.0359	0.0013	0.9842	0.0208	0.0229	0.0009	0.0298	0.985
Neural network	Medium neural network	0.0363	0.0013	0.9839	0.0222	0.0372	0.0034	0.0585	0.9422
Neural network	Bilayered neural network	0.0367	0.0013	0.9835	0.0218	0.0176	0.0005	0.0226	0.9914
Neural network	Trilayered neural network	0.0388	0.0015	0.9816	0.023	0.0449	0.0051	0.0713	0.914
Gaussian process regression	Squared exponential GPR	0.0431	0.0019	0.9773	0.0256	0.0277	0.0015	0.0393	0.9738
Neural network	Wide neural network	0.0438	0.0019	0.9765	0.025	0.0374	0.0027	0.0521	0.9541
Neural network	Narrow neural network	0.052	0.0027	0.9669	0.0293	0.0408	0.0039	0.0621	0.9348
Linear regression	Interactions Linear	0.0535	0.0029	0.965	0.0371	0.0396	0.0025	0.0495	0.9585
Stepwise linear regression	Stepwise linear	0.0554	0.0031	0.9624	0.0394	0.0388	0.0028	0.0528	0.9529
SVM	Medium Gaussian SVM	0.0582	0.0034	0.9585	0.0438	0.0451	0.0026	0.0514	0.9552
Tree	Fine tree	0.0592	0.0035	0.9571	0.0368	0.0328	0.0022	0.0464	0.9636
SVM	Quadratic SVM	0.0617	0.0038	0.9534	0.0476	0.0349	0.0021	0.046	0.9642
Linear regression	Linear	0.0642	0.0041	0.9496	0.0504	0.0416	0.0028	0.0529	0.9526
Linear regression	Robust linear	0.0653	0.0043	0.9478	0.0511	0.0406	0.0026	0.0513	0.9554
SVM	Linear SVM	0.0658	0.0043	0.947	0.0517	0.0411	0.0026	0.0511	0.9558
SVM	Fine Gaussian SVM	0.0717	0.0051	0.9371	0.0518	0.0494	0.0037	0.0611	0.9368
SVM	Cubic SVM	0.075	0.0056	0.9311	0.0545	0.0393	0.0038	0.0617	0.9356
Gaussian process regression	Custom Gaussian process regression	0.0819	0.0067	0.9179	0.0389	0.0549	0.0121	0.1099	0.7955
Ensemble	Boosted trees	0.0819	0.0067	0.9178	0.0495	0.0315	0.0019	0.0435	0.968
Gaussian process regression	Custom Gaussian process regression	0.0877	0.0077	0.9058	0.0663	0.0801	0.0093	0.0965	0.8425
SVM	Coarse Gaussian SVM	0.0945	0.0089	0.8906	0.0702	0.0604	0.0046	0.0677	0.9225
Ensemble	Bagged trees	0.1021	0.0104	0.8724	0.0641	0.0593	0.0091	0.0954	0.8459
Kernel	SVM kernel	0.1349	0.0182	0.7772	0.0947	0.1012	0.0235	0.1534	0.602
Kernel	Least squares regression kernel	0.1473	0.0217	0.7343	0.117	0.1303	0.0247	0.1572	0.5817
Tree	Medium tree	0.1504	0.0226	0.7232	0.0941	0.0546	0.0165	0.1286	0.7204
Tree	Coarse tree	0.2349	0.0552	0.3243	0.1742	0.0967	0.0137	0.1171	0.7679

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