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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

From: Performance prediction of data streams on high-performance architecture

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