ServerRun 36926
CreatorOlavRG
Programsvmlight-linear
DatasetConcentration of 30 compounds for 70H and 70A
Task typeBinaryClassification
Created2y354d ago
Done! Flag_green
1s
25M
BinaryClassification
1s
0.490
0s
0.524
1s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Scanning examples...done
Reading examples into memory...OK. (98 examples read)
Setting default regularization parameter C=0.0000
Optimizing..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (507 iterations)
Optimization finished (48 misclassified, maxdiff=0.00063).
Runtime in cpu-seconds: 0.69
Number of SV: 97 (including 94 at upper bound)
L1 loss: loss=95.90924
Norm of weight vector: |w|=0.00001
Norm of longest example vector: |x|=31103.83904
Estimated VCdim of classifier: VCdim<=1.15559
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=97.96% (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: 29101
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. (97 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 98 incorrect, 98 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. (97 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 42 incorrect, 42 total)
Precision/recall on test set: -nan%/0.00%
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]


real	0m1.915s
user	0m1.396s
sys	0m0.400s

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