vowpalwabbit - Interpreting Vowpal Wabbit results: Why are some lines appended by "h"? -



vowpalwabbit - Interpreting Vowpal Wabbit results: Why are some lines appended by "h"? -

below part of log training vw model.

why of these lines followed h? you'll notice that's true of "average loss" line in summary @ end. i'm not sure means, or if should care.

... average since illustration illustration current current current loss lastly counter weight label predict features 1.000000 1.000000 1 1.0 -1.0000 0.0000 15 0.500000 0.000000 2 2.0 1.0000 1.0000 15 1.250000 2.000000 4 4.0 -1.0000 1.0000 9 1.167489 1.084979 8 8.0 -1.0000 1.0000 29 1.291439 1.415389 16 16.0 1.0000 1.0000 45 1.096302 0.901166 32 32.0 -1.0000 -1.0000 21 1.299807 1.503312 64 64.0 -1.0000 1.0000 7 1.413753 1.527699 128 128.0 -1.0000 1.0000 11 1.459430 1.505107 256 256.0 -1.0000 1.0000 47 1.322658 1.185886 512 512.0 -1.0000 -1.0000 59 1.193357 1.064056 1024 1024.0 -1.0000 1.0000 69 1.145822 1.098288 2048 2048.0 -1.0000 -1.0000 5 1.187072 1.228322 4096 4096.0 -1.0000 -1.0000 9 1.093551 1.000031 8192 8192.0 -1.0000 -1.0000 67 1.041445 0.989338 16384 16384.0 -1.0000 -0.6838 29 1.107593 1.173741 32768 32768.0 1.0000 -1.0000 5 1.147313 1.187034 65536 65536.0 -1.0000 1.0000 7 1.078471 1.009628 131072 131072.0 -1.0000 -1.0000 73 1.004700 1.004700 262144 262144.0 -1.0000 1.0000 41 h 0.918594 0.832488 524288 524288.0 -1.0000 -1.0000 7 h 0.868978 0.819363 1048576 1048576.0 -1.0000 -1.0000 21 h finished run number of examples per pass = 152064 passes used = 10 weighted illustration sum = 1.52064e+06 weighted label sum = -854360 average loss = 0.809741 h ...

thanks

this h printed when

(!all.holdout_set_off && all.current_pass >= 1)

is true (see output grep -nh -e '\<h\\n' vowpalwabbit/*.cc , view code).

search --holdout_off in command line arguments:

--holdout_off disables holdout validation multiple pass learning. default, vw holds out (controllable default = 1/10th) subset of examples whenever --passes > 1 , reports test loss on print out. used prevent overfitting in multiple pass learning. h printed @ end of line specify reported losses holdout validation loss, instead of progressive validation loss.

vowpalwabbit

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