Geek Answers Handbook

Volpal Logical Regression

I am performing a logistic regression using Vowpal Wabbit in a dataset with 25 functions and 48 million instances. I have a question about the current forecast values. If it is within 0 or 1.

average since example example current current current loss last counter weight label predict features 0.693147 0.693147 1 1.0 -1.0000 0.0000 24 0.419189 0.145231 2 2.0 -1.0000 -1.8559 24 0.235457 0.051725 4 4.0 -1.0000 -2.7588 23 6.371911 12.508365 8 8.0 -1.0000 -3.7784 24 3.485084 0.598258 16 16.0 -1.0000 -2.2767 24 1.765249 0.045413 32 32.0 -1.0000 -2.8924 24 1.017911 0.270573 64 64.0 -1.0000 -3.0438 25 0.611419 0.204927 128 128.0 -1.0000 -3.1539 25 0.469127 0.326834 256 256.0 -1.0000 -1.6101 23 0.403473 0.337820 512 512.0 -1.0000 -2.8843 25 0.337348 0.271222 1024 1024.0 -1.0000 -2.5209 25 0.328909 0.320471 2048 2048.0 -1.0000 -2.0732 25 0.309401 0.289892 4096 4096.0 -1.0000 -2.7639 25 0.291447 0.273492 8192 8192.0 -1.0000 -2.5978 24 0.287428 0.283409 16384 16384.0 -1.0000 -3.1774 25 0.287249 0.287071 32768 32768.0 -1.0000 -2.7770 24 0.282737 0.278224 65536 65536.0 -1.0000 -1.9070 25 0.278517 0.274297 131072 131072.0 -1.0000 -3.3813 24 0.291475 0.304433 262144 262144.0 1.0000 -2.7975 23 0.324553 0.357630 524288 524288.0 -1.0000 -0.8995 24 0.373086 0.421619 1048576 1048576.0 -1.0000 -1.2076 24 0.422605 0.472125 2097152 2097152.0 1.0000 -1.4907 25 0.476046 0.529488 4194304 4194304.0 -1.0000 -1.8591 25 0.476627 0.477208 8388608 8388608.0 -1.0000 -2.0037 23 0.446556 0.416485 16777216 16777216.0 -1.0000 -0.9915 24 0.422831 0.399107 33554432 33554432.0 -1.0000 -1.9549 25 0.428316 0.433801 67108864 67108864.0 -1.0000 -0.6376 24 0.425511 0.422705 134217728 134217728.0 -1.0000 -0.4094 24 0.425185 0.424860 268435456 268435456.0 -1.0000 -1.1529 24 0.426747 0.428309 536870912 536870912.0 -1.0000 -2.7468 25
+7
machine-learning logistic-regression vowpalwabbit
source share
1 answer

Forecasts are in the range [-50, +50] (theoretically any real number, but Vowpal Wabbit truncates it to [-50, +50]).

To convert them to {-1, +1}, use --binary . Positive forecasts are simply displayed at +1, minus -1.

To convert them to [0, +1], use --link=logistic . This uses the boolean function 1 / (1 + exp (-x)). You should also use --loss_function=logistic if you want to interpret numbers as probabilities.

To convert them to [-1, +1], use --link=glf1 . This uses the formula 2 / (1 + exp (-x)) - 1 (generalized logistic function with limits 1).

+12
source share

More articles:


All Articles