 | Run 16732 |
| Creator | internal |
| Program | svmlight-linear |
| Dataset | is_bad |
| Task type | BinaryClassification |
| Created | 318d3h ago |
Done! 
| BinaryClassification | |
| 3s | |
| 0.008 | |
| 0s | |
| 0.007 | |
| 0s |
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..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..OK. (4640 examples read) Setting default regularization parameter C=0.0003 Optimizing................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (817 iterations) Optimization finished (37 misclassified, maxdiff=0.00098). Runtime in cpu-seconds: 2.57 Number of SV: 120 (including 114 at upper bound) L1 loss: loss=71.41400 Norm of weight vector: |w|=0.12262 Norm of longest example vector: |x|=10441.51387 Estimated VCdim of classifier: VCdim<=2593.73952 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=2.59% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>89.61% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>89.30% (rho=1.00,depth=0) Number of kernel evaluations: 91640 Writing model file...done === END program1: ./run learn ../dataset2/train --- OK [3s] ===== 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. (120 support vectors read) Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..done Runtime (without IO) in cpu-seconds: 0.06 === 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 [1s] ===== 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. (120 support vectors read) Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..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 [0s] real 0m4.294s user 0m3.828s sys 0m0.324s
Run specification 
Results
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