Performance prediction of data streams on high-performance architecture

Table 1 Comparison of regression algorithm with respect to three evaluation metrics (with cross validation k = 10) performed on (i) Cluster-I, (ii) Cluster-II and (iii) Cluster-I and II

Regressions	Cluster	MSE	RMSE	MAE	R\(^2\)
Lasso regression	C-I	68.9297	8.0056	5.9667	0.9807
Lasso regression	C-II	58.4858	7.5675	5.3386	0.963
Lasso regression	C-III	42.481	6.518	4.649	0.99
Ridge regression	C-I	66.7715	8.1146	5.8574	0.9813
Ridge regression	C-II	55.2465	7.3488	5.2273	0.9663
Ridge regression	C-III	40.433	6.359	4.546	0.991
Elastic net regression	C-I	69.1028	8.0056	5.9757	0.9805
Elastic net regression	C-II	60.7171	7.6966	5.3623	0.9618
Elastic net regression	C-III	42.578	6.525	4.693	0.99
\(\epsilon\)-SVR linear kernel	C-I	316.88	8.1301	13.7762	0.9113
\(\epsilon\)-SVR linear kernel	C-II	298.6252	16.7423	13.3822	0.813
\(\epsilon\)-SVR linear kernel	C-III	126.669	11.255	9.044	0.971
nu-SVR linear kernel	C-I	227.4365	17.0608	11.7427	0.9331
nu-SVR linear kernel	C-II	214.9223	14.2851	10.9677	0.8661
nu-SVR linear kernel	C-III	132.333	11.502	9.289	0.969

C-I, Cluster-I; C-II, Cluster-II; C-III, Cluster-I and Cluster-II
Italic values indicate the significance of various regression techniques (a minimum value of error within clusters)