vowpalwabbit - 解释 Vowpal Wabbit 结果 : Why are some lines appended by "h"?

Question

以下是训练我的大众模型的日志的一部分。

为什么其中一些行后跟 h？您会注意到最后总结中的“平均损失”行也是如此。我不确定这意味着什么，或者我是否应该关心。

...  
average    since         example     example  current  current  current    
loss       last          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 example sum = 1.52064e+06  
weighted label sum = -854360  
average loss = 0.809741 h  
...

谢谢

Answer 1

此 h打印时

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

为真(请参阅 grep -nH -e '\<h\\n' vowpalwabbit/*.cc 的输出并查看代码)。

搜索 --holdout_off在 Command line arguments :

--holdout_off disables holdout validation for multiple pass learning. By default, VW holds out a (controllable default = 1/10th) subset of examples whenever --passes > 1 and reports the test loss on the print out. This is used to prevent overfitting in multiple pass learning. An extra h is printed at the end of the line to specify the reported losses are holdout validation loss, instead of progressive validation loss.