MLcomp version alpha Statistics: 1208 users, 401 datasets, 328 programs, 9937 runs (0 queued, 0 running), 0 workers

Run 7293

Creator	jake
Program	svmlight-linear
Dataset	isolet
Task type	BinaryClassification
Created	1y122d ago
Download	Login required!

Status: Done!

Total Time	11m48s
Max Memory Usage	154M
Domain:	MulticlassClassification
Learn time
Train error	0.205
Predict train time
Test error	0.229
Predict test time

Log file

... (lines omitted) ...
Norm of weight vector: |w|=0.76978
Norm of longest example vector: |x|=17.77566
Estimated VCdim of classifier: VCdim<=167.06311
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=3.86% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>48.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>49.79% (rho=1.00,depth=0)
Number of kernel evaluations: 69979
Writing model file...done
=== END _one-vs-all-learner26: ./run learn ../data26 --- OK [11s]

=== END program1: ./run learn ../dataset3/train --- OK [315s]

===== 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 [2s]
=== 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. (430 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.46% (216 correct, 6022 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.46%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [12s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (468 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.97% (185 correct, 6053 incorrect, 6238 total)
Precision/recall on test set: 100.00%/2.97%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [12s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Reading model...OK. (207 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.80% (237 correct, 6001 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.80%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [12s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4
Reading model...OK. (422 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.03% (189 correct, 6049 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.03%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4 --- OK [12s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5
Reading model...OK. (427 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.13% (195 correct, 6043 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.13%
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5 --- OK [11s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6
Reading model...OK. (459 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 3.17% (198 correct, 6040 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.17%
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6 --- OK [12s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7
Reading model...OK. (435 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.16% (197 correct, 6041 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.16%
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7 --- OK [12s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8
Reading model...OK. (180 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.78% (236 correct, 6002 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.78%
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8 --- OK [12s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9
Reading model...OK. (231 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..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 3.74% (233 correct, 6005 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.74%
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9 --- OK [11s]
=== START _one-vs-all-learner10: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y10
Reading model...OK. (341 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.75% (234 correct, 6004 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.75%
=== END _one-vs-all-learner10: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y10 --- OK [12s]
=== START _one-vs-all-learner11: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y11
Reading model...OK. (424 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.27% (204 correct, 6034 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.27%
=== END _one-vs-all-learner11: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y11 --- OK [12s]
=== START _one-vs-all-learner12: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y12
Reading model...OK. (249 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.75% (234 correct, 6004 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.75%
=== END _one-vs-all-learner12: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y12 --- OK [11s]
=== START _one-vs-all-learner13: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y13
Reading model...OK. (373 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 3.57% (223 correct, 6015 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.57%
=== END _one-vs-all-learner13: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y13 --- OK [12s]
=== START _one-vs-all-learner14: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y14
Reading model...OK. (425 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.16% (197 correct, 6041 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.16%
=== END _one-vs-all-learner14: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y14 --- OK [12s]
=== START _one-vs-all-learner15: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y15
Reading model...OK. (244 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 3.64% (227 correct, 6011 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.64%
=== END _one-vs-all-learner15: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y15 --- OK [11s]
=== START _one-vs-all-learner16: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y16
Reading model...OK. (518 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 1.92% (120 correct, 6118 incorrect, 6238 total)
Precision/recall on test set: 100.00%/1.92%
=== END _one-vs-all-learner16: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y16 --- OK [12s]
=== START _one-vs-all-learner17: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y17
Reading model...OK. (206 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..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 3.69% (230 correct, 6008 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.69%
=== END _one-vs-all-learner17: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y17 --- OK [11s]
=== START _one-vs-all-learner18: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y18
Reading model...OK. (158 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.80% (237 correct, 6001 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.80%
=== END _one-vs-all-learner18: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y18 --- OK [12s]
=== START _one-vs-all-learner19: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y19
Reading model...OK. (239 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..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 3.80% (237 correct, 6001 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.80%
=== END _one-vs-all-learner19: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y19 --- OK [11s]
=== START _one-vs-all-learner20: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y20
Reading model...OK. (445 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.79% (174 correct, 6064 incorrect, 6238 total)
Precision/recall on test set: 100.00%/2.79%
=== END _one-vs-all-learner20: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y20 --- OK [12s]
=== START _one-vs-all-learner21: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y21
Reading model...OK. (196 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..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 3.74% (233 correct, 6005 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.74%
=== END _one-vs-all-learner21: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y21 --- OK [11s]
=== START _one-vs-all-learner22: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y22
Reading model...OK. (450 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.01% (188 correct, 6050 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.01%
=== END _one-vs-all-learner22: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y22 --- OK [12s]
=== START _one-vs-all-learner23: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y23
Reading model...OK. (218 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.67% (229 correct, 6009 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.67%
=== END _one-vs-all-learner23: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y23 --- OK [11s]
=== START _one-vs-all-learner24: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y24
Reading model...OK. (199 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.77% (235 correct, 6003 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.77%
=== END _one-vs-all-learner24: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y24 --- OK [12s]
=== START _one-vs-all-learner25: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y25
Reading model...OK. (111 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.82% (238 correct, 6000 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.82%
=== END _one-vs-all-learner25: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y25 --- OK [11s]
=== START _one-vs-all-learner26: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y26
Reading model...OK. (278 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.70% (231 correct, 6007 incorrect, 6238 total)
Precision/recall on test set: 100.00%/3.70%
=== END _one-vs-all-learner26: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y26 --- OK [11s]
6238 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [303s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [2s]

===== 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 [0s]
=== 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. (430 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.40% (53 correct, 1506 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.40%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [4s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (468 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.44% (38 correct, 1521 incorrect, 1559 total)
Precision/recall on test set: 100.00%/2.44%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [4s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Reading model...OK. (207 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.91% (61 correct, 1498 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.91%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [3s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4
Reading model...OK. (422 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.50% (39 correct, 1520 incorrect, 1559 total)
Precision/recall on test set: 100.00%/2.50%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4 --- OK [4s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5
Reading model...OK. (427 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.89% (45 correct, 1514 incorrect, 1559 total)
Precision/recall on test set: 100.00%/2.89%
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5 --- OK [3s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6
Reading model...OK. (459 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.53% (55 correct, 1504 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.53%
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6 --- OK [4s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7
Reading model...OK. (435 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.01% (47 correct, 1512 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.01%
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7 --- OK [3s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8
Reading model...OK. (180 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.85% (60 correct, 1499 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.85%
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8 --- OK [3s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9
Reading model...OK. (231 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.53% (55 correct, 1504 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.53%
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9 --- OK [4s]
=== START _one-vs-all-learner10: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y10
Reading model...OK. (341 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.66% (57 correct, 1502 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.66%
=== END _one-vs-all-learner10: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y10 --- OK [3s]
=== START _one-vs-all-learner11: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y11
Reading model...OK. (424 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.14% (49 correct, 1510 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.14%
=== END _one-vs-all-learner11: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y11 --- OK [4s]
=== START _one-vs-all-learner12: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y12
Reading model...OK. (249 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.78% (59 correct, 1500 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.78%
=== END _one-vs-all-learner12: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y12 --- OK [3s]
=== START _one-vs-all-learner13: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y13
Reading model...OK. (373 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.40% (53 correct, 1506 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.40%
=== END _one-vs-all-learner13: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y13 --- OK [3s]
=== START _one-vs-all-learner14: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y14
Reading model...OK. (425 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.08% (48 correct, 1511 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.08%
=== END _one-vs-all-learner14: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y14 --- OK [4s]
=== START _one-vs-all-learner15: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y15
Reading model...OK. (244 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.59% (56 correct, 1503 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.59%
=== END _one-vs-all-learner15: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y15 --- OK [3s]
=== START _one-vs-all-learner16: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y16
Reading model...OK. (518 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 1.86% (29 correct, 1530 incorrect, 1559 total)
Precision/recall on test set: 100.00%/1.86%
=== END _one-vs-all-learner16: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y16 --- OK [4s]
=== START _one-vs-all-learner17: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y17
Reading model...OK. (206 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 3.91% (61 correct, 1498 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.91%
=== END _one-vs-all-learner17: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y17 --- OK [3s]
=== START _one-vs-all-learner18: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y18
Reading model...OK. (158 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.85% (60 correct, 1499 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.85%
=== END _one-vs-all-learner18: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y18 --- OK [3s]
=== START _one-vs-all-learner19: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y19
Reading model...OK. (239 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.72% (58 correct, 1501 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.72%
=== END _one-vs-all-learner19: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y19 --- OK [3s]
=== START _one-vs-all-learner20: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y20
Reading model...OK. (445 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.44% (38 correct, 1521 incorrect, 1559 total)
Precision/recall on test set: 100.00%/2.44%
=== END _one-vs-all-learner20: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y20 --- OK [4s]
=== START _one-vs-all-learner21: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y21
Reading model...OK. (196 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.85% (60 correct, 1499 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.85%
=== END _one-vs-all-learner21: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y21 --- OK [3s]
=== START _one-vs-all-learner22: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y22
Reading model...OK. (450 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 2.82% (44 correct, 1515 incorrect, 1559 total)
Precision/recall on test set: 100.00%/2.82%
=== END _one-vs-all-learner22: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y22 --- OK [4s]
=== START _one-vs-all-learner23: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y23
Reading model...OK. (218 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 3.59% (56 correct, 1503 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.59%
=== END _one-vs-all-learner23: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y23 --- OK [3s]
=== START _one-vs-all-learner24: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y24
Reading model...OK. (199 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.78% (59 correct, 1500 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.78%
=== END _one-vs-all-learner24: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y24 --- OK [3s]
=== START _one-vs-all-learner25: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y25
Reading model...OK. (111 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.78% (59 correct, 1500 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.78%
=== END _one-vs-all-learner25: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y25 --- OK [3s]
=== START _one-vs-all-learner26: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y26
Reading model...OK. (278 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 3.21% (50 correct, 1509 incorrect, 1559 total)
Precision/recall on test set: 100.00%/3.21%
=== END _one-vs-all-learner26: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y26 --- OK [3s]
1559 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [89s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	11m51.724s
user	11m34.447s
sys	0m12.353s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) one-vs-all: Reduction from multiclass classification to binary classification.
    • (binaryLearner:Program[BinaryClassification]) svmlight-linear: SVMlight for binary classification using a linear kernel (http://svmlight.joachims.org)
  • (dataset:Dataset) isolet: 7797 examples, 617 features
  • (stripper:Program[Strip]) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.228992944194997
    • numErrors: 357
    • numExamples: 1559
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 4
    • predict10:
      • success: true
      • time: 3
    • predict11:
      • success: true
      • time: 4
    • predict12:
      • success: true
      • time: 3
    • predict13:
      • success: true
      • time: 3
    • predict14:
      • success: true
      • time: 4
    • predict15:
      • success: true
      • time: 3
    • predict16:
      • success: true
      • time: 4
    • predict17:
      • success: true
      • time: 3
    • predict18:
      • success: true
      • time: 3
    • predict19:
      • success: true
      • time: 3
    • predict2:
      • success: true
      • time: 4
    • predict20:
      • success: true
      • time: 4
    • predict21:
      • success: true
      • time: 3
    • predict22:
      • success: true
      • time: 4
    • predict23:
      • success: true
      • time: 3
    • predict24:
      • success: true
      • time: 3
    • predict25:
      • success: true
      • time: 3
    • predict26:
      • success: true
      • time: 3
    • predict3:
      • success: true
      • time: 3
    • predict4:
      • success: true
      • time: 4
    • predict5:
      • success: true
      • time: 3
    • predict6:
      • success: true
      • time: 4
    • predict7:
      • success: true
      • time: 3
    • predict8:
      • success: true
      • time: 3
    • predict9:
      • success: true
      • time: 4
    • success: true
    • time: 89
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.20519397242706
    • numErrors: 1280
    • numExamples: 6238
    • success: true
    • time: 2
  • predict:
    • predict1:
      • success: true
      • time: 12
    • predict10:
      • success: true
      • time: 12
    • predict11:
      • success: true
      • time: 12
    • predict12:
      • success: true
      • time: 11
    • predict13:
      • success: true
      • time: 12
    • predict14:
      • success: true
      • time: 12
    • predict15:
      • success: true
      • time: 11
    • predict16:
      • success: true
      • time: 12
    • predict17:
      • success: true
      • time: 11
    • predict18:
      • success: true
      • time: 12
    • predict19:
      • success: true
      • time: 11
    • predict2:
      • success: true
      • time: 12
    • predict20:
      • success: true
      • time: 12
    • predict21:
      • success: true
      • time: 11
    • predict22:
      • success: true
      • time: 12
    • predict23:
      • success: true
      • time: 11
    • predict24:
      • success: true
      • time: 12
    • predict25:
      • success: true
      • time: 11
    • predict26:
      • success: true
      • time: 11
    • predict3:
      • success: true
      • time: 12
    • predict4:
      • success: true
      • time: 12
    • predict5:
      • success: true
      • time: 11
    • predict6:
      • success: true
      • time: 12
    • predict7:
      • success: true
      • time: 12
    • predict8:
      • success: true
      • time: 12
    • predict9:
      • success: true
      • time: 11
    • success: true
    • time: 303
  • strip:
    • success: true
    • time: 2
• exitCode: 0
• learn:
  • learn1:
    • success: true
    • time: 12
  • learn10:
    • success: true
    • time: 12
  • learn11:
    • success: true
    • time: 11
  • learn12:
    • success: true
    • time: 11
  • learn13:
    • success: true
    • time: 11
  • learn14:
    • success: true
    • time: 12
  • learn15:
    • success: true
    • time: 12
  • learn16:
    • success: true
    • time: 12
  • learn17:
    • success: true
    • time: 11
  • learn18:
    • success: true
    • time: 12
  • learn19:
    • success: true
    • time: 12
  • learn2:
    • success: true
    • time: 12
  • learn20:
    • success: true
    • time: 12
  • learn21:
    • success: true
    • time: 12
  • learn22:
    • success: true
    • time: 12
  • learn23:
    • success: true
    • time: 12
  • learn24:
    • success: true
    • time: 12
  • learn25:
    • success: true
    • time: 11
  • learn26:
    • success: true
    • time: 11
  • learn3:
    • success: true
    • time: 12
  • learn4:
    • success: true
    • time: 12
  • learn5:
    • success: true
    • time: 12
  • learn6:
    • success: true
    • time: 12
  • learn7:
    • success: true
    • time: 12
  • learn8:
    • success: true
    • time: 11
  • learn9:
    • success: true
    • time: 12
  • success: true
  • time: 315
• success: true

Comments: