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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

ModelsNo. estimatorsMeanSTDRMSEMAER2PrecisionRecallAUCTrain timeTest time
DTBotc11.0000.0000.0000.0001.0001.0001.0001.0000.0530.004
SVMBotc10.5960.0630.6190.383− 0.6210.0000.6170.5000.3330.013
MLPBotc10.9140.0550.2330.0540.7700.9530.9460.9380.0410.003
DTBotc51.0000.0000.0000.0001.0001.0001.0001.0000.0700.010
SVMBotc50.5960.0630.6190.383− 0.6210.0000.6170.5001.1570.104
MLPBotc50.9300.0410.1930.0370.8420.9640.9630.9580.2310.050
DTBotc101.0000.0000.0000.0001.0001.0001.0001.0000.0700.010
SVMBotc100.5960.0630.6190.383− 0.6210.0000.6170.5001.1570.104
MLPBotc100.9300.0410.1930.0370.8420.9640.9630.9582.3100.050
DTBotc151.0000.0000.0000.0001.0001.0001.0001.0000.0700.004
SVMBotc150.5960.0630.6190.383− 0.6210.0000.6170.5001.5460.058
MLPBotc150.9460.0480.2020.0410.8280.9320.9590.9604.0200.525
DTBotc201.0000.0000.0000.0001.0001.0001.0001.0000.0770.003
SVMBotc200.5960.0630.6190.383− 0.6210.0000.6170.5001.6940.058
MLPBotc200.9480.0340.1300.0170.9280.9910.9830.9806.6800.720
DTBotc501.0000.0000.0000.0001.0001.0001.0001.0000.0600.004
SVMBotc500.5960.0630.6190.383− 0.6210.0000.6170.5002.8390.037
MLPBotc500.9550.0330.1840.0340.8570.9640.9660.96213.0500.800
DTBotc1001.0000.0000.0000.0001.0001.0001.0001.0000.0690.004
SVMBotc1000.5960.0630.6190.383− 0.6210.0000.6170.5005.7750.058
MLPBotc1000.9520.0260.1750.0310.8710.9560.9690.96950.2004.300
DTBotc1501.0000.0000.0000.0001.0001.0001.0001.0000.0920.004
SVMBotc1500.5960.0630.6190.383− 0.6210.0000.6170.5008.7510.049
MLPBotc1500.1350.0360.1930.0370.8420.9470.9630.96184.30020.250
DTBotc2001.0000.0000.0000.0001.0001.0001.0001.0000.0990.004
SVMBotc2000.5960.0630.6190.383− 0.6210.0000.6170.50011.7990.049
MLPBotc2000.9460.0230.1750.0310.8710.9730.9690.96575.4006.600