ServerRun 39381
Creatorpdoviet
Programsvmlight-linear
DataseteconomicalData
Task typeBinaryClassification
Created2y156d ago
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Done! Flag_green
1m40s
47M
BinaryClassification
1m12s
0.131
19s
0.125
11s

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..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..OK. (10500 examples read)
Setting default regularization parameter C=0.0225
Optimizing................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (5217 iterations)
Optimization finished (1372 misclassified, maxdiff=0.00097).
Runtime in cpu-seconds: 51.35
Number of SV: 3237 (including 2774 at upper bound)
L1 loss: loss=2766.29817
Norm of weight vector: |w|=2.09819
Norm of longest example vector: |x|=7.15072
Estimated VCdim of classifier: VCdim<=226.10769
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.08
XiAlpha-estimate of the error: error<=27.20% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>37.18% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>38.23% (rho=1.00,depth=0)
Number of kernel evaluations: 401591
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [72s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Reading model...OK. (3237 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..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 16.25% (1706 correct, 8794 incorrect, 10500 total)
Precision/recall on test set: 100.00%/16.25%
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [19s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [4s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Reading model...OK. (3237 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 16.33% (735 correct, 3765 incorrect, 4500 total)
Precision/recall on test set: 100.00%/16.33%
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [11s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	1m51.516s
user	1m41.694s
sys	0m2.740s

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