 | Run 7752 |
| Creator | internal |
| Program | svmlight-linear |
| Dataset | tweets.svm.2k |
| Task type | BinaryClassification |
| Created | 1y122d ago |
Done! 
| 1s | |
| 29M | |
| BinaryClassification | |
| 0.043 | |
| 0.085 | |
Log file
===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset2/train Scanning examples...done Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..OK. (1400 examples read) Setting default regularization parameter C=0.0816 Optimizing.............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (750 iterations) Optimization finished (60 misclassified, maxdiff=0.00097). Runtime in cpu-seconds: 0.18 Number of SV: 581 (including 102 at upper bound) L1 loss: loss=103.19340 Norm of weight vector: |w|=2.88624 Norm of longest example vector: |x|=13.26650 Estimated VCdim of classifier: VCdim<=475.83247 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.86% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>53.85% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>64.15% (rho=1.00,depth=0) Number of kernel evaluations: 56938 Writing model file...done === END program1: ./run learn ../dataset2/train --- OK [1s] ===== MAIN: predict/evaluate on train data ===== === START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in === END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [0s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out Reading model...OK. (581 support vectors read) Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..done Runtime (without IO) in cpu-seconds: 0.00 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out === END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s] ===== MAIN: predict/evaluate on test data ===== === START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in === END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out Reading model...OK. (581 support vectors read) Classifying test examples..100..200..300..400..500..600..done Runtime (without IO) in cpu-seconds: 0.00 === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out === END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s] real 0m1.796s user 0m0.640s sys 0m0.100s
Run specification 
Results
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