 | Run 14263 |
| Creator | internal |
| Program | svmlight-linear |
| Dataset | blakhol_bc_test_5000 |
| Task type | BinaryClassification |
| Created | 1y310d ago |
| Download | Login required! |
Done! 
| 1s | |
| 26M | |
| BinaryClassification | |
| 0.424 | |
| 0.421 | |
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..OK. (3500 examples read) Setting default regularization parameter C=0.1161 Optimizing...........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (4924 iterations) Optimization finished (1484 misclassified, maxdiff=0.00087). Runtime in cpu-seconds: 1.51 Number of SV: 3140 (including 3132 at upper bound) L1 loss: loss=3134.19451 Norm of weight vector: |w|=0.49565 Norm of longest example vector: |x|=23.24863 Estimated VCdim of classifier: VCdim<=67.21766 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=89.71% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>9.31% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>9.30% (rho=1.00,depth=0) Number of kernel evaluations: 93695 Writing model file...done === END program1: ./run learn ../dataset2/train --- OK [2s] ===== 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. (3140 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..done Runtime (without IO) in cpu-seconds: 0.01 === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s] === 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. (3140 support vectors read) Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..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 0m3.276s user 0m2.580s sys 0m0.552s
Run specification 
Results
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