Status: Done!
Total Time
4m26s
Max Memory Usage
47M
Domain
MulticlassClassification
Learn time
Train error
0
Predict train time
Test error
0.361
Predict test time
Log file
... (lines omitted) ...
iteration:622 feature:14839 threshold:-1.79769e+308 min-objective:0.993722
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=== END program1: ./run learn ../dataset2/train --- OK [255s]
===== 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
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]
===== 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
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [0s]
real 4m27.628s
user 2m58.775s
sys 0m1.004s
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) person-presence : Predict audio-visual presence of persons in TV shows given speech transcript.
(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: 0.360824742268041
numErrors: 70
numExamples: 194
success: true
time: 0
predict:
strip:
doTrain:
evaluate:
errorRate: 0.0
numErrors: 0
numExamples: 490
success: true
time: 0
predict:
strip:
exitCode: 0
learn:
success: true
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