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Table 11 Bagging ensemble classifiers training time, predicting time and error metrics result on BSE dataset

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

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCtrain timeTest time
DTBagc:11.0000.0000.0000.0001.0001.0001.0001.000.0950.011
SVMBagc:10.9070.0440.2910.0850.6410.9680.9150.890.0960.011
MLPBagc:10.9770.0210.2850.0810.6560.8450.9190.930.9390.045
DTBagc:51.0000.0000.0000.0001.0001.0001.0001.000.2830.024
SVMBagc:50.9110.0350.2540.0640.7270.9800.9360.920.4490.056
MLPBagc:50.9870.0140.1010.0100.9570.9820.9900.993.9290.256
DTBagc:101.0000.0000.0000.0001.0001.0001.0001.000.3030.068
SVMBagc:100.9110.0280.2790.0780.6700.9890.9220.900.7510.135
MLPBagc:100.9930.0120.0580.0030.9860.9910.9971.009.6630.739
DTBagc:151.0000.0000.0000.0001.0001.0001.0001.000.5500.036
SVMBagc:150.9110.0280.2790.0780.6700.9890.9220.901.5160.230
MLPBagc:150.9910.0120.0000.0001.0001.0001.0001.0012.2320.855
DTBagc:201.0000.0000.0000.0001.0001.0001.0001.002.5110.060
SVMBagc:200.9620.0240.1540.0240.9000.9910.9760.975.0480.225
MLPBagc:200.9960.0070.0000.0001.0001.0001.0001.00103.2081.127
DTBagc:501.0000.0000.0000.0001.0001.0001.0001.001.2540.124
SVMBagc:500.9170.0290.1540.0240.9000.9910.9760.974.2450.525
MLPBagc:500.9930.0100.0000.0001.0001.0001.0001.0058.6503.288
DTBagc:1001.0000.0000.0000.0001.0001.0001.0001.002.5090.234
SVMBagc:1000.9160.0310.2730.0750.6840.9890.9250.909.7701.066
MLPBagc:1000.9940.0100.0000.0001.0001.0001.0001.00107.3267.407
DTBagc:1501.0000.0000.0000.0001.0001.0001.0001.003.7940.395
SVMBagc:1500.9140.0320.2850.0810.6560.9890.9190.9014.1221.345
MLPBagc:1500.9940.0100.0000.0001.0001.0001.0001.000.0109.248
DTBagc:2001.0000.0000.0000.0001.0001.0001.0001.004.6280.554
SVMBagc:2000.9130.0330.2850.0810.6561.0000.9190.8916.8741.963
MLPBagc:2000.9940.0100.0000.0001.0001.0001.0001.00248.90315.745