11name,value,precision,higherIsBetter
2- VowpalWabbitRegressor_energyefficiency2012_data.train.csv_,8.137509233528203 ,1.0,false
3- VowpalWabbitRegressor_energyefficiency2012_data.train.csv_--adaptive,12.960062725747207 ,1.0,false
4- VowpalWabbitRegressor_airfoil_self_noise.train.csv_,70.43269163082503 ,0.1,false
5- VowpalWabbitRegressor_airfoil_self_noise.train.csv_--adaptive,17.476643558307703 ,0.1,false
6- VowpalWabbitRegressor_Buzz.TomsHardware.train.csv_,13293.0085777787 ,1000.0,false
7- VowpalWabbitRegressor_Buzz.TomsHardware.train.csv_--adaptive,4064.9979055539925 ,1000.0,false
8- VowpalWabbitRegressor_machine.train.csv_,178.66842858742254 ,100.0,false
2+ VowpalWabbitRegressor_energyefficiency2012_data.train.csv_,8.137487567593242 ,1.0,false
3+ VowpalWabbitRegressor_energyefficiency2012_data.train.csv_--adaptive,14.63517235995779 ,1.0,false
4+ VowpalWabbitRegressor_airfoil_self_noise.train.csv_,70.43180227391186 ,0.1,false
5+ VowpalWabbitRegressor_airfoil_self_noise.train.csv_--adaptive,16.802540944080658 ,0.1,false
6+ VowpalWabbitRegressor_Buzz.TomsHardware.train.csv_,13293.008827393687 ,1000.0,false
7+ VowpalWabbitRegressor_Buzz.TomsHardware.train.csv_--adaptive,13210.937569470014 ,1000.0,false
8+ VowpalWabbitRegressor_machine.train.csv_,178.66830300708224 ,100.0,false
99VowpalWabbitRegressor_machine.train.csv_--adaptive,183.5800804363435,100.0,false
10- VowpalWabbitRegressor_Concrete_Data.train.csv_,15.735532284668981 ,1.0,false
11- VowpalWabbitRegressor_Concrete_Data.train.csv_--adaptive,39.51882087437542,1.0,false
10+ VowpalWabbitRegressor_Concrete_Data.train.csv_,15.735495289179807 ,1.0,false
11+ VowpalWabbitRegressor_Concrete_Data.train.csv_--adaptive,39.51882087437542,1.0,false
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