Table 19 Stacking and blending ensemble classifiers error metrics result over GSE dataset
From: A comprehensive evaluation of ensemble learning for stock-market prediction
Model | Mean | STD | RMSE | MAE | R2 | Precision | Recall | AUC | Train time | Test time |
|---|---|---|---|---|---|---|---|---|---|---|
STK_DSN_C | 0.896 | 0.022 | 0.257 | 0.066 | 0.7255 | 0.939 | 0.934 | 0.930 | 2.443 | 0.131 |
STK_SND_C | 0.960 | 0.019 | 0.000 | 0.000 | 1.0000 | 1.000 | 1.000 | 1.000 | 9.966 | 0.837 |
STK_DNS_C | 0.960 | 0.019 | 0.000 | 0.000 | 1.0000 | 1.000 | 1.000 | 1.000 | 7.615 | 0.188 |
Vote(DSN) | 0.918 | 0.032 | 0.170 | 0.029 | 0.8799 | 0.966 | 0.971 | 0.967 | 9.285 | 0.223 |
BLD_DSN_C | 0.849 | 0.048 | 0.363 | 0.132 | 0.4509 | 0.856 | 0.868 | 0.851 | 23.455 | 2.783 |
BLD_SND_C | 0.942 | 0.024 | 0.272 | 0.074 | 0.6911 | 1.000 | 0.926 | 0.938 | 20.041 | 0.771 |
BLD_DNS_C | 0.959 | 0.018 | 0.000 | 0.000 | 1.0000 | 1.000 | 1.000 | 1.000 | 19.658 | 1.102 |