machine learning - Vowpal Wabbit not predicting binary values, maybe overtraining? -


i trying use vowpal wabbit binary classification, i.e. given feature values vw classify either 1 or 0. how have training data formatted.

1 'name | feature1:0 feature2:1 feature3:48 feature4:4881 ... -1 'name2 | feature1:1 feature2:0 feature3:5 feature4:2565 ... etc

i have 30,000 1 data points, , 3,000 0 data points. have 100 1 , 100 0 data points i'm using test on, after create model. these test data points classified default 1. here how format prediction set:

1 'name | feature1:0 feature2:1 feature3:48 feature4:4881 ...

from understanding of vw documentation, need use either logistic or hinge loss_function binary classifications. how i've been creating model:

vw -d ../training_set.txt --loss_function logistic/hinge -f model

and how try predictions:

vw -d ../test_set.txt --loss_function logistic/hinge -i model -t -p /dev/stdout

however, i'm running problems. if use hinge loss function, predictions -1. when use logistic loss function, arbitrary values between 5 , 11. there general trend data points should 0 lower values, 5-7, , data points should 1 6-11. doing wrong? i've looked around documentation , checked bunch of articles vw see if can identify problem is, can't figure out. ideally 0,1 value, or value between 0 , 1 corresponds how strong vw thinks result is. appreciated!

independently of tool and/or specific algorithm can use "learning curves" ,and train/cross validation/test splitting diagnose algorithm , determine whats problem . after diagnosing problem can apply adjustments algorithm, example if find have over-fitting can apply actions like:

  1. add regularization
  2. get more training data
  3. reduce complexity of model
  4. eliminate redundant features.

you can reference andrew ng. "advice machine learning" videos on youtube more details on subject.


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