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Table 9 Boosting ensemble classifiers training time, prediction time and error metrics result on JSE dataset

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

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTrain timeTest time
DTBotc10.5870.0290.6600.436− 0.7520.5490.5640.5350.1610.006
SVMBotc10.5270.0350.6850.469− 0.8820.5310.5310.5003.2980.023
MLPBotc10.5730.0510.6730.453− 0.8210.5440.5470.5233.3000.035
DTBotc50.6080.0360.6530.427− 0.7130.5550.5730.5450.4720.020
SVMBotc50.5270.0350.6850.469− 0.8820.5310.5310.5005.1100.062
MLPBotc50.6440.0920.5240.274− 0.1010.9190.7260.7395.3500.650
DTBotc100.6420.0360.5120.262− 0.0840.6640.7180.7031.1220.086
SVMBotc100.5170.0340.6750.459− 0.8720.5210.5210.5004.5600.103
MLPBotc100.6590.0780.6280.398− 0.6270.5560.5820.5558.9500.400
DTBotc150.6520.0370.5220.272− 0.0940.6740.7280.7131.2220.096
SVMBotc150.5270.0350.6850.469− 0.8820.5310.5310.5004.6600.203
MLPBotc150.6690.0790.6380.408− 0.6370.5660.5920.56513.9500.450
DTBotc200.6380.0420.4340.1890.2430.7490.8110.8011.8250.092
SVMBotc200.5270.0350.6850.469− 0.8820.5310.5310.5004.5700.078
MLPBotc200.7050.1100.5110.261− 0.0480.8320.7390.74612.5000.900
DTBotc500.6790.0320.3600.1300.4800.8250.8700.8644.3600.214
SVMBotc500.5270.0350.6850.469− 0.8820.5310.5310.5006.5200.061
MLPBotc500.6970.1010.6900.476− 0.9120.5770.5240.5338.1000.300
DTBotc1000.7070.0320.2000.0400.8390.9610.9600.9608.4200.580
SVMBotc1000.5270.0350.6850.469− 0.8820.5310.5310.5005.7320.077
MLPBotc1000.7360.0890.5540.307− 0.2320.6730.6930.68513.2500.117
DTBotc1500.7350.0340.1570.0250.9010.9820.9750.97615.7990.884
SVMBotc1500.5270.0350.6850.469− 0.8820.5310.5310.5005.1530.085
MLPBotc1500.6880.0570.6530.427− 0.7130.5580.5730.54927.5001.020
DTBotc2000.7380.0350.0620.0040.9850.9960.9960.99617.4070.996
SVMBotc2000.5270.0350.6850.469− 0.8820.5310.5310.5007.0200.531
MLPBotc2000.7060.0910.5970.356− 0.4300.6110.6440.62631.6000.644