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creating quadratic features for pairs: tr
final_regressor = models/0002a.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
learning_rate set to 10
average since example example current current current
loss last counter weight label predict features
0.116985 0.116985 3 3.0 0.5498 0.3393 15
0.072802 0.028618 6 6.0 0.2681 0.0000 15
0.064328 0.054159 11 11.0 0.4315 0.0000 15
0.041313 0.018298 22 22.0 0.5519 0.5597 15
0.022418 0.003522 44 44.0 0.5514 0.6180 15
0.014116 0.005621 87 87.0 0.5140 0.5066 15
0.009419 0.004722 174 174.0 0.5596 0.5548 15
0.006447 0.003475 348 348.0 0.5475 0.5501 15
0.004132 0.001817 696 696.0 0.3421 0.3947 15
0.002638 0.001144 1392 1392.0 0.4996 0.5066 15
0.001607 0.000575 2784 2784.0 0.5090 0.5234 15
0.001024 0.000442 5568 5568.0 0.6413 0.6181 15
0.000809 0.000594 11135 11135.0 0.3869 0.4344 15
0.000614 0.000418 22269 22269.0 0.5063 0.5065 15
0.000633 0.000651 44537 44537.0 0.4905 0.4800 15
finished run
number of examples = 74746
weighted example sum = 6.952e+04
weighted label sum = 3.511e+04
average loss = 0.0006521
best constant = 0.5051
best constant's loss = 0.25
total feature number = 1121190
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