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final_regressor = models/0002.model
using no cache
Reading from train-sets/0002.dat
num sources = 1
Num weight bits = 18
learning rate = 10
initial_t = 1
power_t = 0.5
learning_rate set to 10
average since example example current current current
loss last counter weight label predict features
0.110447 0.110447 3 3.0 0.5498 0.3591 15
0.070578 0.030710 6 6.0 0.2681 0.0000 15
0.064146 0.056427 11 11.0 0.4315 0.0000 15
0.039332 0.014519 22 22.0 0.5519 0.5562 15
0.021432 0.003532 44 44.0 0.5514 0.6163 15
0.013416 0.005214 87 87.0 0.5140 0.5052 15
0.008898 0.004379 174 174.0 0.5596 0.5639 15
0.006059 0.003221 348 348.0 0.5475 0.5427 15
0.004075 0.002091 696 696.0 0.3421 0.4014 15
0.002947 0.001819 1392 1392.0 0.4996 0.4716 15
0.002098 0.001249 2784 2784.0 0.5090 0.5203 15
0.001704 0.001310 5568 5568.0 0.6413 0.5771 15
0.001770 0.001835 11135 11135.0 0.3869 0.4407 15
0.002000 0.002231 22269 22269.0 0.5063 0.5228 15
0.002340 0.002680 44537 44537.0 0.4905 0.4860 15
finished run
number of examples = 74746
weighted example sum = 6.952e+04
weighted label sum = 3.511e+04
average loss = 0.002412
best constant = 0.5051
best constant's loss = 0.25
total feature number = 1121190
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