MLcomp version alpha Statistics: 2040 users, 637 datasets, 389 programs, 18661 runs (0 queued, 2 running), 0 workers

Run 1799

Creator	pliang
Program	svmlight_multiclass-linear
Dataset	annealing
Task type	MulticlassClassification
Created	2y222d ago
Download	Login required!

Status: Done!

Total Time	41m33s
Max Memory Usage	36M
Domain	MulticlassClassification
Learn time
Train error	0
Predict train time
Test error	0
Predict test time

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset6/train
=== START program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test
=== END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test --- OK [0s]
===== Cross-validator: trying hyperparameter 0.01 =====
=== START _tune-hyperparameter0: ./run setHyperparameter 0.01
=== END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s]
=== START _tune-hyperparameter0: ./run learn ../cv.train
Using hyperparameter c = 0.01
Reading training examples... (78 examples) done
Training set properties: 55 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=323.8826, QPEps=0.0000)
Iter 3: *(NumConst=3, SV=3, CEps=216.2210, QPEps=0.0000)
Iter 4: *(NumConst=4, SV=3, CEps=33.7067, QPEps=1.9388)
Iter 5: *(NumConst=5, SV=4, CEps=21.1726, QPEps=7.4003)
Iter 6: .........*(NumConst=6, SV=4, CEps=11.1139, QPEps=4.5448)
Iter 7: *(NumConst=7, SV=5, CEps=1.7284, QPEps=0.8613)
Iter 8: *(NumConst=8, SV=5, CEps=5.8042, QPEps=0.0123)
Iter 9: .........*(NumConst=9, SV=5, CEps=0.7571, QPEps=0.2427)
Iter 10: *(NumConst=10, SV=6, CEps=0.3745, QPEps=0.1110)
Iter 11: *(NumConst=11, SV=6, CEps=0.1522, QPEps=0.0626)
Iter 12: .........(NumConst=11, SV=6, CEps=0.0373, QPEps=0.0626)
Final epsilon on KKT-Conditions: 0.06257
Upper bound on duality gap: 0.00017
Dual objective value: dval=0.91222
Primal objective value: pval=0.91239
Total number of constraints in final working set: 11 (of 11)
Number of iterations: 12
Number of calls to 'find_most_violated_constraint': 312
Number of SV: 6 
Norm of weight vector: |w|=0.20133
Value of slack variable (on working set): xi=89.21250
Value of slack variable (global): xi=89.21250
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=830.73912
Runtime in cpu-seconds: 1.25
Final number of constraints in cache: 156
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0
Reading model...done.
Reading test examples... (33 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 54.5455
Zero/one-error on test set: 54.55% (15 correct, 18 incorrect, 33 total)
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [0s]
CV error rate 0.545454545454545 with hyperparameter 0.01

===== Cross-validator: trying hyperparameter 0.1 =====
=== START _tune-hyperparameter1: ./run setHyperparameter 0.1
=== END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s]
=== START _tune-hyperparameter1: ./run learn ../cv.train
Using hyperparameter c = 0.1
Reading training examples... (78 examples) done
Training set properties: 55 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=323.8826, QPEps=0.0000)
Iter 3: *(NumConst=3, SV=3, CEps=693.8920, QPEps=0.0007)
Iter 4: *(NumConst=4, SV=3, CEps=28.6587, QPEps=0.2994)
Iter 5: *(NumConst=5, SV=4, CEps=23.2680, QPEps=11.1861)
Iter 6: .........*(NumConst=6, SV=3, CEps=12.0857, QPEps=4.4124)
Iter 7: *(NumConst=7, SV=3, CEps=4.8285, QPEps=0.7774)
Iter 8: *(NumConst=8, SV=4, CEps=2.1959, QPEps=1.0525)
Iter 9: .........*(NumConst=9, SV=3, CEps=1.2133, QPEps=0.4417)
Iter 10: *(NumConst=10, SV=4, CEps=0.7409, QPEps=0.3700)
Iter 11: *(NumConst=11, SV=4, CEps=0.4411, QPEps=0.2204)
Iter 12: *(NumConst=12, SV=4, CEps=0.4714, QPEps=0.1330)
Iter 13: *(NumConst=13, SV=5, CEps=0.2406, QPEps=0.1194)
Iter 14: .........*(NumConst=14, SV=4, CEps=0.1194, QPEps=0.0238)
Iter 15: .........(NumConst=14, SV=4, CEps=0.0381, QPEps=0.0238)
Final epsilon on KKT-Conditions: 0.03812
Upper bound on duality gap: 0.00326
Dual objective value: dval=7.80853
Primal objective value: pval=7.81179
Total number of constraints in final working set: 14 (of 14)
Number of iterations: 15
Number of calls to 'find_most_violated_constraint': 390
Number of SV: 4 
Norm of weight vector: |w|=1.46854
Value of slack variable (on working set): xi=67.32052
Value of slack variable (global): xi=67.33479
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=831.41726
Runtime in cpu-seconds: 3.59
Final number of constraints in cache: 156
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [4s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1
Reading model...done.
Reading test examples... (33 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 21.2121
Zero/one-error on test set: 21.21% (26 correct, 7 incorrect, 33 total)
=== END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [0s]
CV error rate 0.212121212121212 with hyperparameter 0.1

===== Cross-validator: trying hyperparameter 1.0 =====
=== START _tune-hyperparameter2: ./run setHyperparameter 1.0
=== END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s]
=== START _tune-hyperparameter2: ./run learn ../cv.train
Using hyperparameter c = 1.0
Reading training examples... (78 examples) done
Training set properties: 55 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=323.8826, QPEps=0.0000)
Iter 3: *(NumConst=3, SV=3, CEps=693.8920, QPEps=0.0004)
Iter 4: *(NumConst=4, SV=4, CEps=41.6730, QPEps=14.9731)
Iter 5: .........*(NumConst=5, SV=4, CEps=16.9685, QPEps=8.4586)
Iter 6: *(NumConst=6, SV=4, CEps=31.7120, QPEps=8.3154)
Iter 7: *(NumConst=7, SV=4, CEps=20.3598, QPEps=8.4649)
Iter 8: *(NumConst=8, SV=5, CEps=16.9681, QPEps=8.4168)
Iter 9: *(NumConst=9, SV=6, CEps=9.8600, QPEps=4.8678)
Iter 10: *(NumConst=10, SV=5, CEps=5.0198, QPEps=2.4162)
Iter 11: *(NumConst=11, SV=6, CEps=6.1071, QPEps=2.4201)
Iter 12: *(NumConst=12, SV=7, CEps=2.5589, QPEps=1.2760)
Iter 13: .........*(NumConst=13, SV=6, CEps=2.6243, QPEps=1.3115)
Iter 14: *(NumConst=14, SV=6, CEps=2.4708, QPEps=1.2346)
Iter 15: *(NumConst=15, SV=7, CEps=0.7355, QPEps=0.3675)
Iter 16: .........(NumConst=15, SV=7, CEps=0.0938, QPEps=0.3675)
Final epsilon on KKT-Conditions: 0.36746
Upper bound on duality gap: 0.22447
Dual objective value: dval=53.06195
Primal objective value: pval=53.28642
Total number of constraints in final working set: 15 (of 15)
Number of iterations: 16
Number of calls to 'find_most_violated_constraint': 312
Number of SV: 7 
Norm of weight vector: |w|=4.29374
Value of slack variable (on working set): xi=43.97456
Value of slack variable (global): xi=44.06833
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=831.41726
Runtime in cpu-seconds: 69.92
Final number of constraints in cache: 167
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [70s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
Reading model...done.
Reading test examples... (33 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 21.2121
Zero/one-error on test set: 21.21% (26 correct, 7 incorrect, 33 total)
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s]
CV error rate 0.212121212121212 with hyperparameter 1.0

===== Cross-validator: trying hyperparameter 10.0 =====
=== START _tune-hyperparameter3: ./run setHyperparameter 10.0
=== END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s]
=== START _tune-hyperparameter3: ./run learn ../cv.train
Using hyperparameter c = 10.0
Reading training examples... (78 examples) done
Training set properties: 55 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=323.8826, QPEps=0.0000)
Iter 3: *(NumConst=3, SV=3, CEps=693.8920, QPEps=0.0004)
Iter 4: *(NumConst=4, SV=3, CEps=41.6730, QPEps=18.2176)
Iter 5: *(NumConst=5, SV=5, CEps=122.2927, QPEps=19.9303)
Iter 6: *(NumConst=6, SV=4, CEps=11.8687, QPEps=5.9283)
Iter 7: *(NumConst=7, SV=6, CEps=20.9517, QPEps=5.9303)
Iter 8: *(NumConst=8, SV=6, CEps=38.5171, QPEps=1.7858)
Iter 9: .........*(NumConst=9, SV=7, CEps=8.8475, QPEps=4.3990)
Iter 10: *(NumConst=10, SV=6, CEps=15.8638, QPEps=4.4185)
Iter 11: *(NumConst=11, SV=7, CEps=6.5295, QPEps=2.9761)
Iter 12: *(NumConst=12, SV=7, CEps=4.9584, QPEps=2.4730)
Iter 13: *(NumConst=13, SV=6, CEps=3.3213, QPEps=1.5296)
Iter 14: *(NumConst=14, SV=7, CEps=1.2569, QPEps=0.6275)
Iter 15: .........*(NumConst=15, SV=9, CEps=0.9123, QPEps=1.3423)
Iter 16: *(NumConst=16, SV=9, CEps=1.2239, QPEps=0.4553)
Iter 17: *(NumConst=17, SV=10, CEps=0.5386, QPEps=0.2689)
Iter 18: *(NumConst=18, SV=8, CEps=0.3515, QPEps=0.1757)
Iter 19: *(NumConst=19, SV=8, CEps=0.3266, QPEps=0.1632)
Iter 20: *(NumConst=20, SV=8, CEps=0.4512, QPEps=0.1252)
Iter 21: *(NumConst=21, SV=9, CEps=0.2405, QPEps=0.0879)
Iter 22: .........(NumConst=21, SV=9, CEps=0.0704, QPEps=0.0879)
Final epsilon on KKT-Conditions: 0.08789
Upper bound on duality gap: 0.86701
Dual objective value: dval=427.67036
Primal objective value: pval=428.53737
Total number of constraints in final working set: 21 (of 21)
Number of iterations: 22
Number of calls to 'find_most_violated_constraint': 312
Number of SV: 9 
Norm of weight vector: |w|=8.06617
Value of slack variable (on working set): xi=39.55461
Value of slack variable (global): xi=39.60058
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=831.41726
Runtime in cpu-seconds: 394.63
Final number of constraints in cache: 162
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [396s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
Reading model...done.
Reading test examples... (33 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 21.2121
Zero/one-error on test set: 21.21% (26 correct, 7 incorrect, 33 total)
=== END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s]
CV error rate 0.212121212121212 with hyperparameter 10.0

===== Cross-validator: trying hyperparameter 100.0 =====
=== START _tune-hyperparameter4: ./run setHyperparameter 100.0
=== END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s]
=== START _tune-hyperparameter4: ./run learn ../cv.train
Using hyperparameter c = 100.0
Reading training examples... (78 examples) done
Training set properties: 55 features, 2 classes
Iter 1: .........*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000)
Iter 2: *(NumConst=2, SV=2, CEps=323.8826, QPEps=0.0000)
Iter 3: *(NumConst=3, SV=3, CEps=693.8920, QPEps=0.0004)
Iter 4: *(NumConst=4, SV=3, CEps=41.6730, QPEps=0.1364)
Iter 5: *(NumConst=5, SV=3, CEps=272.1627, QPEps=13.5672)
Iter 6: *(NumConst=6, SV=4, CEps=56.3724, QPEps=0.4093)
Iter 7: .........*(NumConst=7, SV=5, CEps=12.1387, QPEps=6.0618)
Iter 8: *(NumConst=8, SV=7, CEps=10.6115, QPEps=5.3051)
Iter 9: *(NumConst=9, SV=6, CEps=12.0180, QPEps=5.3047)
Iter 10: *(NumConst=10, SV=7, CEps=8.9113, QPEps=4.4482)
Iter 11: *(NumConst=11, SV=8, CEps=7.8002, QPEps=8.7163)
Iter 12: *(NumConst=12, SV=7, CEps=6.8608, QPEps=3.3676)
Iter 13: *(NumConst=13, SV=6, CEps=2.8415, QPEps=1.4198)
Iter 14: *(NumConst=14, SV=6, CEps=3.2046, QPEps=1.4183)
Iter 15: *(NumConst=15, SV=6, CEps=2.0216, QPEps=1.0107)
Iter 16: *(NumConst=16, SV=9, CEps=1.8876, QPEps=0.6035)
Iter 17: *(NumConst=17, SV=7, CEps=2.8204, QPEps=0.1403)
Iter 18: *(NumConst=18, SV=8, CEps=2.5786, QPEps=0.7719)
Iter 19: *(NumConst=19, SV=7, CEps=1.5989, QPEps=0.6082)
Iter 20: *(NumConst=20, SV=7, CEps=1.8012, QPEps=0.1539)
Iter 21: .........*(NumConst=21, SV=7, CEps=1.6476, QPEps=0.0960)
Iter 22: *(NumConst=22, SV=8, CEps=1.4114, QPEps=0.4905)
Iter 23: *(NumConst=23, SV=9, CEps=1.1657, QPEps=0.5461)
Iter 24: *(NumConst=24, SV=8, CEps=1.2646, QPEps=0.4330)
Iter 25: *(NumConst=25, SV=6, CEps=0.6591, QPEps=0.1079)
Iter 26: *(NumConst=26, SV=7, CEps=0.3781, QPEps=0.1725)
Iter 27: *(NumConst=27, SV=7, CEps=0.6776, QPEps=0.0134)
Iter 28: *(NumConst=28, SV=7, CEps=0.3664, QPEps=0.0039)
Iter 29: *(NumConst=29, SV=8, CEps=0.2559, QPEps=0.1021)
Iter 30: *(NumConst=30, SV=8, CEps=0.2792, QPEps=0.1229)
Iter 31: *(NumConst=31, SV=8, CEps=0.4955, QPEps=0.0198)
Iter 32: *(NumConst=32, SV=7, CEps=0.2171, QPEps=0.0350)
Iter 33: *(NumConst=33, SV=9, CEps=0.1679, QPEps=0.1839)
Iter 34: *(NumConst=34, SV=8, CEps=0.1912, QPEps=13.9487)
Iter 35: *(NumConst=35, SV=10, CEps=0.4513, QPEps=0.0837)
Iter 36: *(NumConst=36, SV=10, CEps=0.1914, QPEps=0.0839)
Iter 37: .........*(NumConst=37, SV=10, CEps=0.1603, QPEps=0.0801)
Iter 38: *(NumConst=38, SV=10, CEps=0.1418, QPEps=0.0697)
Iter 39: *(NumConst=39, SV=9, CEps=0.1429, QPEps=0.0260)
Iter 40: *(NumConst=40, SV=10, CEps=0.1573, QPEps=0.0515)
Iter 41: .........(NumConst=40, SV=10, CEps=0.0648, QPEps=0.0515)
Final epsilon on KKT-Conditions: 0.06478
Upper bound on duality gap: 9.58139
Dual objective value: dval=2646.36332
Primal objective value: pval=2655.94471
Total number of constraints in final working set: 40 (of 40)
Number of iterations: 41
Number of calls to 'find_most_violated_constraint': 390
Number of SV: 10 
Norm of weight vector: |w|=50.47193
Value of slack variable (on working set): xi=13.75902
Value of slack variable (global): xi=13.82237
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=831.41726
Runtime in cpu-seconds: 2021.61
Final number of constraints in cache: 156
Compacting linear model...done
Writing learned model...done
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [2027s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
Reading model...done.
Reading test examples... (33 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 0.0000
Zero/one-error on test set: 0.00% (33 correct, 0 incorrect, 33 total)
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s]
=== START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4
=== END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s]
CV error rate 0.0 with hyperparameter 100.0

Best hyperparameter value is 100.0; got CV error rate 0.0
=== END program1: ./run learn ../dataset6/train --- OK [2498s]

===== MAIN: predict/evaluate on train data =====
=== START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in
=== END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
Reading model...done.
Reading test examples... (111 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 45.9459
Zero/one-error on test set: 45.95% (60 correct, 51 incorrect, 111 total)
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [0s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in
=== END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out
Reading model...done.
Reading test examples... (15 examples) done.
Classifying test examples...done
Runtime (without IO) in cpu-seconds: 0.00
Average loss on test set: 53.3333
Zero/one-error on test set: 53.33% (7 correct, 8 incorrect, 15 total)
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [0s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [0s]


real	41m38.413s
user	41m31.532s
sys	0m0.252s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) tune-hyperparameter: Sets the hyperparameter
    • (numProbes:int) 5
    • (learner:Program) svmlight_multiclass-linear: SVMlight for multiclass classification (http://svmlight.joachims.org/svm_multiclass.html)
    • (splitter:Program) multiclass-utils: Validates and inspects a dataset in MulticlassClassification format.
    • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
  • (dataset:Dataset) annealing: 126 examples, 55 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: 0.0
    • numErrors: 0
    • numExamples: 15
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0
    • numErrors: 0
    • numExamples: 111
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • bestCVErrorRate: 0.0
  • bestHyperparameter: 100.0
  • evaluate0:
    • errorRate: 0.545454545454545
    • numErrors: 18
    • numExamples: 33
    • success: true
    • time: 0
  • evaluate1:
    • errorRate: 0.212121212121212
    • numErrors: 7
    • numExamples: 33
    • success: true
    • time: 0
  • evaluate2:
    • errorRate: 0.212121212121212
    • numErrors: 7
    • numExamples: 33
    • success: true
    • time: 0
  • evaluate3:
    • errorRate: 0.212121212121212
    • numErrors: 7
    • numExamples: 33
    • success: true
    • time: 0
  • evaluate4:
    • errorRate: 0.0
    • numErrors: 0
    • numExamples: 33
    • success: true
    • time: 0
  • learn0:
    • success: true
    • time: 1
  • learn1:
    • success: true
    • time: 4
  • learn2:
    • success: true
    • time: 70
  • learn3:
    • success: true
    • time: 396
  • learn4:
    • success: true
    • time: 2027
  • predict0:
    • success: true
    • time: 0
  • predict1:
    • success: true
    • time: 0
  • predict2:
    • success: true
    • time: 0
  • predict3:
    • success: true
    • time: 0
  • predict4:
    • success: true
    • time: 0
  • setHyperparameter0:
    • success: true
    • time: 0
  • setHyperparameter1:
    • success: true
    • time: 0
  • setHyperparameter2:
    • success: true
    • time: 0
  • setHyperparameter3:
    • success: true
    • time: 0
  • setHyperparameter4:
    • success: true
    • time: 0
  • split:
    • success: true
    • time: 0
  • success: true
  • time: 2498
• success: true

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