Table 8 Boosting ensemble classifiers accuracy and error metrics result on NYSE dataset
From: A comprehensive evaluation of ensemble learning for stock-market prediction
Models | No. estimators | Mean | STD | RMSE | MAE | R2 | Precision | Recall | AUC | Train time | Test time |
|---|---|---|---|---|---|---|---|---|---|---|---|
DTBotc | 1.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.051 | 0.003 |
SVMBotc | 1.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 0.966 | 0.021 |
MLPBotc | 1.000 | 0.953 | 0.040 | 0.257 | 0.066 | 0.734 | 0.952 | 0.934 | 0.934 | 1.500 | 0.051 |
DTBotc | 5.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.053 | 0.003 |
SVMBotc | 5.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 2.898 | 0.129 |
MLPBotc | 5.000 | 0.976 | 0.016 | 0.062 | 0.004 | 0.985 | 1.000 | 0.996 | 0.996 | 3.100 | 0.200 |
DTBotc | 10.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.058 | 0.004 |
SVMBotc | 10.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 4.050 | 0.022 |
MLPBotc | 10.000 | 0.977 | 0.032 | 0.222 | 0.049 | 0.802 | 0.988 | 0.951 | 0.953 | 2.660 | 0.150 |
DTBotc | 15.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.058 | 0.005 |
SVMBotc | 15.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 4.850 | 0.013 |
MLPBotc | 15.000 | 0.977 | 0.032 | 0.222 | 0.049 | 0.802 | 0.988 | 0.951 | 0.953 | 3.990 | 0.225 |
DTBotc | 20.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.057 | 0.003 |
SVMBotc | 20.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 3.667 | 0.109 |
MLPBotc | 20.000 | 0.965 | 0.061 | 0.163 | 0.027 | 0.894 | 0.955 | 0.973 | 0.972 | 5.440 | 0.260 |
DTBotc | 50.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.057 | 0.004 |
SVMBotc | 50.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 3.697 | 0.112 |
MLPBotc | 50.000 | 0.975 | 0.032 | 0.199 | 0.040 | 0.840 | 0.971 | 0.960 | 0.961 | 17.800 | 1.100 |
DTBotc | 100.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.060 | 0.144 |
SVMBotc | 100.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 4.592 | 0.112 |
MLPBotc | 100.000 | 0.976 | 0.032 | 0.250 | 0.063 | 0.749 | 0.955 | 0.938 | 0.938 | 21.100 | 0.500 |
DTBotc | 150.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.059 | 0.004 |
SVMBotc | 150.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 4.213 | 0.115 |
MLPBotc | 150.000 | 0.989 | 0.010 | 0.151 | 0.023 | 0.909 | 0.962 | 0.977 | 0.976 | 57.000 | 1.350 |
DTBotc | 200.000 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.075 | 0.006 |
SVMBotc | 200.000 | 0.531 | 0.035 | 0.688 | 0.473 | − 0.899 | 0.527 | 0.527 | 0.500 | 4.466 | 0.212 |
MLPBotc | 200.000 | 0.984 | 0.016 | 0.107 | 0.011 | 0.954 | 0.979 | 0.989 | 0.988 | 54.800 | 5.400 |