我尝试使用VW在一小组示例（约3112）上训练回归模型。我想我正确地做到了，但它给我带来了奇怪的结果。挖了一下，但没有找到任何有用的东西。
$ cat sh600000.feat | vw --l1 1e-8 --l2 1e-8 --readable_model model -b 24 --passes 10 --cache_file cache
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
using cache_file = cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.040000 0.040000 1 1.0 -0.2000 0.0000 79
0.051155 0.062310 2 2.0 0.2000 -0.0496 79
0.046606 0.042056 4 4.0 0.4100 0.1482 79
0.052160 0.057715 8 8.0 0.0200 0.0021 78
0.064936 0.077711 16 16.0 -0.1800 0.0547 77
0.060507 0.056079 32 32.0 0.0000 0.3164 79
0.136933 0.213358 64 64.0 -0.5900 -0.0850 79
0.151692 0.166452 128 128.0 0.0700 0.0060 79
0.133965 0.116238 256 256.0 0.0900 -0.0446 78
0.179995 0.226024 512 512.0 0.3700 -0.0217 79
0.109296 0.038597 1024 1024.0 0.1200 -0.0728 79
0.579360 1.049425 2048 2048.0 -0.3700 -0.0084 79
0.485389 0.485389 4096 4096.0 1.9600 0.3934 79 h
0.517748 0.550036 8192 8192.0 0.0700 0.0334 79 h
finished run
number of examples per pass = 2847
passes used = 5
weighted example sum = 14236
weighted label sum = -155.98
average loss = 0.490685 h
best constant = -0.0109567
total feature number = 1121506
$ wc model
41 48 657 model
问题：
为什么输出（可读）模型中的要素数量少于实际要素数量？我指出训练数据包含78个特征（加上训练期间显示的79的偏差）。特征位的数量是24，这应该足以避免碰撞。
为什么平均损失实际上会在培训中上升，如上例所示？
（次要）我试图将功能位数增加到32，然后输出一个空模型。为什么呢？
编辑：
我试图改变输入文件，以及使用--holdout_off，如建议的那样。但结果仍然几乎相同 - 平均损失上升。
$ cat sh600000.feat.shuf | vw --l1 1e-8 --l2 1e-8 --readable_model model -b 24 --passes 10 --cache_file cache --holdout_off
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
using cache_file = cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.040000 0.040000 1 1.0 -0.2000 0.0000 79
0.051155 0.062310 2 2.0 0.2000 -0.0496 79
0.046606 0.042056 4 4.0 0.4100 0.1482 79
0.052160 0.057715 8 8.0 0.0200 0.0021 78
0.071332 0.090504 16 16.0 0.0300 0.1203 79
0.043720 0.016108 32 32.0 -0.2200 -0.1971 78
0.142895 0.242071 64 64.0 0.0100 -0.1531 79
0.158564 0.174232 128 128.0 0.0500 -0.0439 79
0.150691 0.142818 256 256.0 0.3200 0.1466 79
0.197050 0.243408 512 512.0 0.2300 -0.0459 79
0.117398 0.037747 1024 1024.0 0.0400 0.0284 79
0.636949 1.156501 2048 2048.0 1.2500 -0.0152 79
0.363364 0.089779 4096 4096.0 0.1800 0.0071 79
0.477569 0.591774 8192 8192.0 -0.4800 0.0065 79
0.411068 0.344567 16384 16384.0 0.0700 0.0450 77
finished run
number of examples per pass = 3112
passes used = 10
weighted example sum = 31120
weighted label sum = -105.5
average loss = 0.423404
best constant = -0.0033901
total feature number = 2451800
训练样例彼此独特，所以我怀疑是否存在过度拟合问题（据我所知，通常在输入数量与特征数量相比较小时发生）。
EDIT2：
尝试打印每个示例的平均损失，并看到它大部分保持不变。
$ cat dist/sh600000.feat | vw --l1 1e-8 --l2 1e-8 -f dist/model -P 3112 --passes 10 -b 24 --cache_file dist/cache
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
final_regressor = dist/model
using cache_file = dist/cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.498822 0.498822 3112 3112.0 0.0800 0.0015 79 h
0.476677 0.454595 6224 6224.0 -0.2200 -0.0085 79 h
0.466413 0.445856 9336 9336.0 0.0200 -0.0022 79 h
0.490221 0.561506 12448 12448.0 0.0700 -0.1113 79 h
finished run
number of examples per pass = 2847
passes used = 5
weighted example sum = 14236
weighted label sum = -155.98
average loss = 0.490685 h
best constant = -0.0109567
total feature number = 1121506
另外尝试没有--l1， - l2和-b参数：
$ cat dist/sh600000.feat | vw -f dist/model -P 3112 --passes 10 --cache_file dist/cacheNum weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
final_regressor = dist/model
using cache_file = dist/cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.520286 0.520286 3112 3112.0 0.0800 -0.0021 79 h
0.488581 0.456967 6224 6224.0 -0.2200 -0.0137 79 h
0.474247 0.445538 9336 9336.0 0.0200 -0.0299 79 h
0.496580 0.563450 12448 12448.0 0.0700 -0.1727 79 h
0.533413 0.680958 15560 15560.0 -0.1700 0.0322 79 h
0.524531 0.480201 18672 18672.0 -0.9800 -0.0573 79 h
finished run
number of examples per pass = 2801
passes used = 7
weighted example sum = 19608
weighted label sum = -212.58
average loss = 0.491739 h
best constant = -0.0108415
total feature number = 1544713
这是否意味着平均损失在一次传球中上升是正常的，但只要多次传球得到同样的损失就可以了，那么它很好吗？
答案 0 :(得分：2)
--l1 我建议您从github
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