ServerRun 30438
Creatorjinjingwen
Programsvmlight-linear
Datasett14
Task typeBinaryClassification
Created3y287d ago
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Done! Flag_green
1s
24M
BinaryClassification
0s
0.219
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0.231
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Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Scanning examples...done
Reading examples into memory...100..OK. (151 examples read)
Setting default regularization parameter C=0.1228
Optimizing...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (660 iterations)
Optimization finished (33 misclassified, maxdiff=0.00100).
Runtime in cpu-seconds: 0.18
Number of SV: 92 (including 50 at upper bound)
L1 loss: loss=65.95881
Norm of weight vector: |w|=0.06414
Norm of longest example vector: |x|=8.54400
Estimated VCdim of classifier: VCdim<=1.30030
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=55.63% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 38031
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [0s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Reading model...OK. (92 support vectors read)
Classifying test examples..100..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 151 incorrect, 151 total)
Precision/recall on test set: -nan%/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. (92 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 65 incorrect, 65 total)
Precision/recall on test set: -nan%/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	0m1.526s
user	0m0.736s
sys	0m0.276s

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