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Table 8 Boosting ensemble classifiers accuracy and error metrics result on NYSE dataset

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

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTrain timeTest time
DTBotc1.0001.0000.0000.0000.0001.0001.0001.0001.0000.0510.003
SVMBotc1.0000.5310.0350.6880.473− 0.8990.5270.5270.5000.9660.021
MLPBotc1.0000.9530.0400.2570.0660.7340.9520.9340.9341.5000.051
DTBotc5.0001.0000.0000.0000.0001.0001.0001.0001.0000.0530.003
SVMBotc5.0000.5310.0350.6880.473− 0.8990.5270.5270.5002.8980.129
MLPBotc5.0000.9760.0160.0620.0040.9851.0000.9960.9963.1000.200
DTBotc10.0001.0000.0000.0000.0001.0001.0001.0001.0000.0580.004
SVMBotc10.0000.5310.0350.6880.473− 0.8990.5270.5270.5004.0500.022
MLPBotc10.0000.9770.0320.2220.0490.8020.9880.9510.9532.6600.150
DTBotc15.0001.0000.0000.0000.0001.0001.0001.0001.0000.0580.005
SVMBotc15.0000.5310.0350.6880.473− 0.8990.5270.5270.5004.8500.013
MLPBotc15.0000.9770.0320.2220.0490.8020.9880.9510.9533.9900.225
DTBotc20.0001.0000.0000.0000.0001.0001.0001.0001.0000.0570.003
SVMBotc20.0000.5310.0350.6880.473− 0.8990.5270.5270.5003.6670.109
MLPBotc20.0000.9650.0610.1630.0270.8940.9550.9730.9725.4400.260
DTBotc50.0001.0000.0000.0000.0001.0001.0001.0001.0000.0570.004
SVMBotc50.0000.5310.0350.6880.473− 0.8990.5270.5270.5003.6970.112
MLPBotc50.0000.9750.0320.1990.0400.8400.9710.9600.96117.8001.100
DTBotc100.0001.0000.0000.0000.0001.0001.0001.0001.0000.0600.144
SVMBotc100.0000.5310.0350.6880.473− 0.8990.5270.5270.5004.5920.112
MLPBotc100.0000.9760.0320.2500.0630.7490.9550.9380.93821.1000.500
DTBotc150.0001.0000.0000.0000.0001.0001.0001.0001.0000.0590.004
SVMBotc150.0000.5310.0350.6880.473− 0.8990.5270.5270.5004.2130.115
MLPBotc150.0000.9890.0100.1510.0230.9090.9620.9770.97657.0001.350
DTBotc200.0001.0000.0000.0000.0001.0001.0001.0001.0000.0750.006
SVMBotc200.0000.5310.0350.6880.473− 0.8990.5270.5270.5004.4660.212
MLPBotc200.0000.9840.0160.1070.0110.9540.9790.9890.98854.8005.400