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加入实验输出（批次 11/14）

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gloamxun
2026-09-23 23:36:24 +08:00
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deep_learning/Q1/outputs/method_comparison/emotion_summary.csv
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method,n,accuracy_mean,accuracy_std,macro_f1_mean,macro_f1_std,mae_mean,mae_std,pearson_mean,pearson_std
M1,5,0.5900000000000001,0.1673320053068151,0.43700352733686065,0.22785227549938045,0.5521364179253578,0.12144893660249381,0.5305163430956757,0.09576372130151391
M2,5,0.55,0.1767766952966369,0.3434301254301254,0.14084701638388486,0.5691992919147014,0.11997495602498047,0.5082439027512137,0.09789171743073
M3,15,0.5766666666666667,0.18503538915054937,0.43789820467598245,0.21990438278507196,0.5534388141334057,0.11230839313313754,0.5457241196963684,0.09265676731571848
M4,15,0.56,0.15142183273415838,0.41446068467573843,0.13578816810911878,0.6231901267170906,0.13326862372482587,0.5067829543507287,0.1021700904550588
1 method n accuracy_mean accuracy_std macro_f1_mean macro_f1_std mae_mean mae_std pearson_mean pearson_std
2 M1 5 0.5900000000000001 0.1673320053068151 0.43700352733686065 0.22785227549938045 0.5521364179253578 0.12144893660249381 0.5305163430956757 0.09576372130151391
3 M2 5 0.55 0.1767766952966369 0.3434301254301254 0.14084701638388486 0.5691992919147014 0.11997495602498047 0.5082439027512137 0.09789171743073
4 M3 15 0.5766666666666667 0.18503538915054937 0.43789820467598245 0.21990438278507196 0.5534388141334057 0.11230839313313754 0.5457241196963684 0.09265676731571848
5 M4 15 0.56 0.15142183273415838 0.41446068467573843 0.13578816810911878 0.6231901267170906 0.13326862372482587 0.5067829543507287 0.1021700904550588