ServerRun 36692
CreatorGregSen
Programsvmlight-linear
Dataset161 shuffle 2
Task typeBinaryClassification
Created3y35d ago
Done! Flag_green
3s
25M
BinaryClassification
2s
0.415
2s
0.472
2s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Scanning examples...done
Reading examples into memory...100..OK. (123 examples read)
Setting default regularization parameter C=0.0000
Optimizing............................................................................................done. (93 iterations)
Optimization finished (51 misclassified, maxdiff=0.00047).
Runtime in cpu-seconds: 0.13
Number of SV: 111 (including 99 at upper bound)
L1 loss: loss=100.15818
Norm of weight vector: |w|=0.00133
Norm of longest example vector: |x|=3631.83604
Estimated VCdim of classifier: VCdim<=24.27099
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=87.80% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>8.62% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>8.33% (rho=1.00,depth=0)
Number of kernel evaluations: 6623
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. (111 support vectors read)
Classifying test examples..100..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 39.84% (49 correct, 74 incorrect, 123 total)
Precision/recall on test set: 100.00%/39.84%
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [2s]
=== 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. (111 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 35.85% (19 correct, 34 incorrect, 53 total)
Precision/recall on test set: 100.00%/35.85%
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m5.967s
user	0m5.300s
sys	0m0.424s

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