machine learning - How to get training accuracy in svmlight with cross validation -


i want run cross validation on training set using svmlight. seems option -x 1 (although i'm not sure how many folds implements...). output is:

xialpha-estimate of error: error<=31.76% (rho=1.00,depth=0) xialpha-estimate of recall: recall=>68.24% (rho=1.00,depth=0) xialpha-estimate of precision: precision=>69.02% (rho=1.00,depth=0) number of kernel evaluations: 56733 computing leave-one-out **lots of gibberish here** retrain on full problem..............done. leave-one-out estimate of error: error=12.46% leave-one-out estimate of recall: recall=86.39% leave-one-out estimate of precision: precision=88.82% actual leave-one-outs computed:  412 (rho=1.00) runtime leave-one-out in cpu-seconds: 0.84

how can accuracy? estimate of error?

thank you!

these contradicting concepts. training error error on training set, while cross validation used approximate validation error (on data not used training).

your output suggests using n-folds (where n-size of training set) leads called "leave 1 out" validation (only 1 testing point!) overestimating model's quality. should try 10-folds, , accuracy 1-error.


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