Status: Done!
Total Time
7m20s
Max Memory Usage
78M
Domain
MulticlassClassification
Learn time
7m5s
Train error
0.217
Predict train time
3s
Test error
0.238
Predict test time
1s
Log file
... (lines omitted) ...
iteration:622 feature:39 threshold:157.58 min-objective:0.999945
iteration:623 feature:39 threshold:146.465 min-objective:0.999944
iteration:624 feature:39 threshold:95.08 min-objective:0.999951
iteration:625 feature:39 threshold:102.865 min-objective:0.999963
iteration:626 feature:39 threshold:155.68 min-objective:0.999956
iteration:627 feature:39 threshold:163.59 min-objective:0.999952
iteration:628 feature:39 threshold:353.945 min-objective:0.999957
iteration:629 feature:39 threshold:327.885 min-objective:0.999966
iteration:630 feature:39 threshold:157.58 min-objective:0.999959
iteration:631 feature:39 threshold:163.59 min-objective:0.999961
iteration:632 feature:39 threshold:216.81 min-objective:0.99996
iteration:633 feature:39 threshold:231.005 min-objective:0.999961
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iteration:956 feature:15 threshold:4.5 min-objective:0.999974
iteration:957 feature:22 threshold:0.0806682 min-objective:0.999969
iteration:958 feature:16 threshold:2.5 min-objective:0.999979
iteration:959 feature:4 threshold:10.3623 min-objective:0.999977
iteration:960 feature:38 threshold:2572.98 min-objective:0.999966
iteration:961 feature:15 threshold:7.5 min-objective:0.999976
iteration:962 feature:40 threshold:0.856881 min-objective:0.99997
iteration:963 feature:8 threshold:111.5 min-objective:0.999965
iteration:964 feature:10 threshold:12.5 min-objective:0.999966
iteration:965 feature:18 threshold:11.5 min-objective:0.999974
iteration:966 feature:18 threshold:12.5 min-objective:0.999974
iteration:967 feature:40 threshold:0.696918 min-objective:0.999974
iteration:968 feature:4 threshold:14.998 min-objective:0.999977
iteration:969 feature:8 threshold:124.5 min-objective:0.999978
iteration:970 feature:38 threshold:2232.72 min-objective:0.999976
iteration:971 feature:4 threshold:0.0108996 min-objective:0.999979
iteration:972 feature:25 threshold:23.5 min-objective:0.99998
iteration:973 feature:5 threshold:28 min-objective:0.999974
iteration:974 feature:6 threshold:16.5 min-objective:0.999975
iteration:975 feature:76 threshold:-1.79769e+308 min-objective:0.99998
iteration:976 feature:39 threshold:87.495 min-objective:0.99998
iteration:977 feature:39 threshold:83.15 min-objective:0.999958
iteration:978 feature:39 threshold:87.495 min-objective:0.999969
iteration:979 feature:39 threshold:95.08 min-objective:0.999972
iteration:980 feature:39 threshold:102.865 min-objective:0.999971
iteration:981 feature:39 threshold:95.08 min-objective:0.999975
iteration:982 feature:39 threshold:102.865 min-objective:0.999979
iteration:983 feature:4 threshold:16.2171 min-objective:0.999981
iteration:984 feature:39 threshold:95.08 min-objective:0.999982
iteration:985 feature:21 threshold:0.72 min-objective:0.999982
iteration:986 feature:28 threshold:1.5 min-objective:0.999977
iteration:987 feature:15 threshold:0.5 min-objective:0.999975
iteration:988 feature:22 threshold:0.0045638 min-objective:0.999972
iteration:989 feature:21 threshold:1.335 min-objective:0.999975
iteration:990 feature:21 threshold:1.785 min-objective:0.999981
iteration:991 feature:21 threshold:2.163 min-objective:0.999976
iteration:992 feature:21 threshold:2.408 min-objective:0.999972
iteration:993 feature:21 threshold:2.285 min-objective:0.999974
iteration:994 feature:21 threshold:2.095 min-objective:0.999973
iteration:995 feature:21 threshold:1.94 min-objective:0.999974
iteration:996 feature:4 threshold:1.29046 min-objective:0.999979
iteration:997 feature:18 threshold:3.5 min-objective:0.999973
iteration:998 feature:38 threshold:127.037 min-objective:0.999973
iteration:999 feature:4 threshold:0.761397 min-objective:0.999977
=== END program1: ./run learn ../dataset2/train --- OK [425s]
===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [3s]
===== 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
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]
real 7m21.103s
user 7m7.047s
sys 0m7.208s
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) minimalist-boost : Minimalist implementation of boostexter's "scored-text" threshold-based weak learners. Source code included.
(dataset:Dataset) cemst-auditor-performance-prediction2-asr4 : Complementary Evaluation Measures for Speech Transcription, train-test split for task auditor-performance-prediction with asr4, reduced feature set
(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).
doTest:
evaluate:
errorRate: 0.238191462490528
numErrors: 943
numExamples: 3959
success: true
time: 0
predict:
strip:
doTrain:
evaluate:
errorRate: 0.217076468296707
numErrors: 9473
numExamples: 43639
success: true
time: 3
predict:
strip:
exitCode: 0
learn:
success: true
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