Table 7 Boosting ensemble classifiers training time, prediction time and error metrics result on bse 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 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.053 | 0.004 |
SVMBotc | 1 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 0.333 | 0.013 |
MLPBotc | 1 | 0.914 | 0.055 | 0.233 | 0.054 | 0.770 | 0.953 | 0.946 | 0.938 | 0.041 | 0.003 |
DTBotc | 5 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.070 | 0.010 |
SVMBotc | 5 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 1.157 | 0.104 |
MLPBotc | 5 | 0.930 | 0.041 | 0.193 | 0.037 | 0.842 | 0.964 | 0.963 | 0.958 | 0.231 | 0.050 |
DTBotc | 10 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.070 | 0.010 |
SVMBotc | 10 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 1.157 | 0.104 |
MLPBotc | 10 | 0.930 | 0.041 | 0.193 | 0.037 | 0.842 | 0.964 | 0.963 | 0.958 | 2.310 | 0.050 |
DTBotc | 15 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.070 | 0.004 |
SVMBotc | 15 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 1.546 | 0.058 |
MLPBotc | 15 | 0.946 | 0.048 | 0.202 | 0.041 | 0.828 | 0.932 | 0.959 | 0.960 | 4.020 | 0.525 |
DTBotc | 20 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.077 | 0.003 |
SVMBotc | 20 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 1.694 | 0.058 |
MLPBotc | 20 | 0.948 | 0.034 | 0.130 | 0.017 | 0.928 | 0.991 | 0.983 | 0.980 | 6.680 | 0.720 |
DTBotc | 50 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.060 | 0.004 |
SVMBotc | 50 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 2.839 | 0.037 |
MLPBotc | 50 | 0.955 | 0.033 | 0.184 | 0.034 | 0.857 | 0.964 | 0.966 | 0.962 | 13.050 | 0.800 |
DTBotc | 100 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.069 | 0.004 |
SVMBotc | 100 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 5.775 | 0.058 |
MLPBotc | 100 | 0.952 | 0.026 | 0.175 | 0.031 | 0.871 | 0.956 | 0.969 | 0.969 | 50.200 | 4.300 |
DTBotc | 150 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.092 | 0.004 |
SVMBotc | 150 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 8.751 | 0.049 |
MLPBotc | 150 | 0.135 | 0.036 | 0.193 | 0.037 | 0.842 | 0.947 | 0.963 | 0.961 | 84.300 | 20.250 |
DTBotc | 200 | 1.000 | 0.000 | 0.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.099 | 0.004 |
SVMBotc | 200 | 0.596 | 0.063 | 0.619 | 0.383 | − 0.621 | 0.000 | 0.617 | 0.500 | 11.799 | 0.049 |
MLPBotc | 200 | 0.946 | 0.023 | 0.175 | 0.031 | 0.871 | 0.973 | 0.969 | 0.965 | 75.400 | 6.600 |