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Table 13 Bagging ensemble classifiers training time, predicting time and error metrics result on JSE dataset

From: A comprehensive evaluation of ensemble learning for stock-market prediction

ModelsNo. of estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTraining timeTest time
DTBagc:10.6980.0400.4120.1700.3190.8350.8300.8290.3660.012
SVMBagc:10.5550.0330.6830.467− 0.8740.5320.5330.5020.3350.013
MLPBagc:10.8090.0570.4360.1900.2350.7860.8100.8052.2800.187
DTBagc:50.7360.0320.1850.0340.8620.9640.9660.9650.4900.028
SVMBagc:50.5850.0620.6830.467− 0.8740.5320.5330.5021.5510.039
MLPBagc:50.8640.0170.4360.1900.2350.7900.8100.80511.7821.043
DTBagc:100.7510.0340.1450.0210.9160.9930.9790.9800.7630.053
SVMBagc:100.5860.0690.6690.448− 0.7980.5750.5520.5492.8400.066
MLPBagc:100.8630.0220.4320.1870.2500.8010.8130.81023.9231.489
DTBagc:150.7720.0270.1070.0110.9540.9890.9890.9890.9780.107
SVMBagc:150.5680.0470.6500.423− 0.6980.5640.5770.5564.8690.124
MLPBagc:150.8670.0220.4280.1830.2660.8020.8170.81439.9003.282
DTBagc:200.7600.0320.1230.0150.9390.9890.9850.9851.5020.245
SVMBagc:200.5630.0490.6260.392− 0.5760.5860.6080.5895.2800.236
MLPBagc:200.8670.0240.4340.1890.2430.8020.8110.80839.3252.625
DTBagc:500.7670.0380.0440.0020.9920.9960.9980.9983.1090.188
SVMBagc:500.5690.0590.6830.467− 0.8740.5320.5330.50218.0130.362
MLPBagc:500.8690.0210.4320.1870.2500.8030.8130.810132.85210.885
DTBagc:1000.7700.0340.0000.0001.0001.0001.0001.0008.9230.432
SVMBagc:1000.5680.0590.6790.461− 0.8510.5360.5390.50839.5650.802
MLPBagc:1000.8680.0220.4340.1890.2430.8020.8110.808270.82522.026
DTBagc:1500.7700.0310.0000.0001.0001.0001.0001.00011.4460.432
SVMBagc:1500.5650.0560.6790.461− 0.8510.5360.5390.50858.7710.802
MLPBagc:1500.8680.0200.4340.1890.2430.8020.8110.808395.45322.026
DTBagc:2000.7710.0340.0000.0001.0001.0001.0001.00013.7600.772
SVMBagc:2000.5630.0560.6830.467− 0.8740.5320.5330.50263.8581.388
MLPBagc:2000.8700.0230.4340.1890.2430.7980.8110.808548.32833.102