Status: Done!
Total Time
39s
Max Memory Usage
34M
Domain:
DocumentClassification
Learn time
Train error
0.946
Predict train time
Test error
0.951
Predict test time
Log file
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Majority label sci.med out of 20 labels has 713/13180 examples
=== END program1: ./run learn ../dataset2/train --- OK [0s]
===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
Processing examples of label comp.windows.x...
Processing examples of label sci.electronics...
Processing examples of label comp.sys.ibm.pc.hardware...
Processing examples of label rec.sport.hockey...
Processing examples of label soc.religion.christian...
Processing examples of label misc.forsale...
Processing examples of label talk.politics.mideast...
Processing examples of label comp.sys.mac.hardware...
Processing examples of label comp.os.ms-windows.misc...
Processing examples of label rec.motorcycles...
Processing examples of label talk.religion.misc...
Processing examples of label rec.sport.baseball...
Processing examples of label talk.politics.guns...
Processing examples of label sci.crypt...
Processing examples of label alt.atheism...
Processing examples of label sci.space...
Processing examples of label comp.graphics...
Processing examples of label rec.autos...
Processing examples of label talk.politics.misc...
Processing examples of label sci.med...
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [28s]
=== 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
Processing examples of label comp.windows.x...
Processing examples of label sci.electronics...
Processing examples of label comp.sys.ibm.pc.hardware...
Processing examples of label rec.sport.hockey...
Processing examples of label soc.religion.christian...
Processing examples of label misc.forsale...
Processing examples of label talk.politics.mideast...
Processing examples of label comp.sys.mac.hardware...
Processing examples of label comp.os.ms-windows.misc...
Processing examples of label rec.motorcycles...
Processing examples of label talk.religion.misc...
Processing examples of label rec.sport.baseball...
Processing examples of label talk.politics.guns...
Processing examples of label sci.crypt...
Processing examples of label alt.atheism...
Processing examples of label sci.space...
Processing examples of label comp.graphics...
Processing examples of label rec.autos...
Processing examples of label talk.politics.misc...
Processing examples of label sci.med...
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [12s]
=== 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 0m40.754s
user 0m3.448s
sys 0m20.665s
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) document-classification-majority : Chooses the majority class based on training data.
(dataset:Dataset) 20news-18828 : 20 newsgroups dataset for document classification (http://people.csail.mit.edu/jrennie/20Newsgroups)
(stripper:Program[Strip]) document-classification-utils : Inspects DocumentClassification datasets and evaluates DocumentClassification performance.
(evaluator:Program[Evaluate]) document-classification-utils : Inspects DocumentClassification datasets and evaluates DocumentClassification performance.
doTest:
evaluate:
errorRate: 0.950956090651558
numErrors: 5371
numExamples: 5648
success: true
time: 0
predict:
strip:
doTrain:
evaluate:
errorRate: 0.945902883156297
numErrors: 12467
numExamples: 13180
success: true
time: 0
predict:
strip:
exitCode: 0
learn:
success: true
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