ServerRun 39825
Creatorjmatos
Programsvmlight-linear
DatasetIBM_std
Task typeBinaryClassification
Created2y41d ago
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Done! Flag_green
11s
28M
BinaryClassification
8s
0.481
2s
0.474
2s

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..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..OK. (9392 examples read)
Setting default regularization parameter C=0.0571
Optimizing...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (5568 iterations)
Optimization finished (4518 misclassified, maxdiff=0.00077).
Runtime in cpu-seconds: 5.59
Number of SV: 9163 (including 9145 at upper bound)
L1 loss: loss=9147.14841
Norm of weight vector: |w|=0.67627
Norm of longest example vector: |x|=44.33248
Estimated VCdim of classifier: VCdim<=899.83119
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=97.56% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.46% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.46% (rho=1.00,depth=0)
Number of kernel evaluations: 427594
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [8s]

===== 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. (9163 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 22.75% (2137 correct, 7255 incorrect, 9392 total)
Precision/recall on test set: 100.00%/22.75%
=== 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 [1s]

===== 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. (9163 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 23.75% (956 correct, 3069 incorrect, 4025 total)
Precision/recall on test set: 100.00%/23.75%
=== 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	0m12.991s
user	0m11.605s
sys	0m0.972s

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