ServerRun 38257
Creatorchuertas
Programsvmlight-linear
DatasetFS Challenge Dexter
Task typeBinaryClassification
Created2y246d ago
Done! Flag_green
2s
28M
MulticlassClassification
1s
0.237
0s
0.410
0s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
===== One versus all: training label y=1 versus the rest =====
=== START _one-vs-all-learner1: ./run learn ../data1
Scanning examples...done
Reading examples into memory...100..200..300..OK. (300 examples read)
Setting default regularization parameter C=0.0000
Optimizing.................................................................................................done. (98 iterations)
Optimization finished (0 misclassified, maxdiff=0.00089).
Runtime in cpu-seconds: 0.02
Number of SV: 262 (including 42 at upper bound)
L1 loss: loss=9.32589
Norm of weight vector: |w|=0.00964
Norm of longest example vector: |x|=1504.94585
Estimated VCdim of classifier: VCdim<=211.34466
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=36.00% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>70.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>62.50% (rho=1.00,depth=0)
Number of kernel evaluations: 9121
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [0s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
Scanning examples...done
Reading examples into memory...100..200..300..OK. (300 examples read)
Setting default regularization parameter C=0.0000
Optimizing.................................................................................................done. (98 iterations)
Optimization finished (0 misclassified, maxdiff=0.00089).
Runtime in cpu-seconds: 0.08
Number of SV: 262 (including 42 at upper bound)
L1 loss: loss=9.32589
Norm of weight vector: |w|=0.00964
Norm of longest example vector: |x|=1504.94585
Estimated VCdim of classifier: VCdim<=211.34466
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=36.00% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>58.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>65.91% (rho=1.00,depth=0)
Number of kernel evaluations: 9121
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [1s]

=== END program1: ./run learn ../dataset3/train --- OK [1s]

===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1
Reading model...OK. (262 support vectors read)
Classifying test examples..100..200..300..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 50.00% (150 correct, 150 incorrect, 300 total)
Precision/recall on test set: 100.00%/50.00%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [0s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (262 support vectors read)
Classifying test examples..100..200..300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 50.00% (150 correct, 150 incorrect, 300 total)
Precision/recall on test set: 100.00%/50.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s]
300 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in
=== END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1
Reading model...OK. (262 support vectors read)
Classifying test examples..100..200..300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 55.33% (166 correct, 134 incorrect, 300 total)
Precision/recall on test set: 100.00%/55.33%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [0s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (262 support vectors read)
Classifying test examples..100..200..300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 44.67% (134 correct, 166 incorrect, 300 total)
Precision/recall on test set: 100.00%/44.67%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s]
300 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m2.824s
user	0m1.904s
sys	0m0.712s

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