ServerRun 43148
CreatorHossein
Programsvmlight-linear
DatasetCancer
Task typeBinaryClassification
Created1y189d ago
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Done! Flag_green
1s
29M
BinaryClassification
1s
0.338
0s
0.360
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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..200..300..400..500..OK. (517 examples read)
Setting default regularization parameter C=0.0286
Optimizing............................................................................................................................................................................................done. (189 iterations)
Optimization finished (175 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.17
Number of SV: 391 (including 379 at upper bound)
L1 loss: loss=369.85903
Norm of weight vector: |w|=0.66034
Norm of longest example vector: |x|=8.06226
Estimated VCdim of classifier: VCdim<=29.34276
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=74.08% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>3.52% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>3.54% (rho=1.00,depth=0)
Number of kernel evaluations: 16683
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. (391 support vectors read)
Classifying test examples..100..200..300..400..500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 21.66% (112 correct, 405 incorrect, 517 total)
Precision/recall on test set: 100.00%/21.66%
=== 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. (391 support vectors read)
Classifying test examples..100..200..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 19.82% (44 correct, 178 incorrect, 222 total)
Precision/recall on test set: 100.00%/19.82%
=== 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	0m1.368s
user	0m0.824s
sys	0m0.412s

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