MLcomp version alpha Statistics: 1403 users, 461 datasets, 360 programs, 16577 runs (0 queued, 0 running), 0 workers

Run 13929

Creator	Aditya
Program	AttributeSelectedClassifier_weka_nominal
Dataset	Simple Bio-info Glycosylation Data
Task type	MulticlassClassification
Created	28d17h ago
Download	Login required!

Status: Done!

Total Time	16s
Max Memory Usage	428M
Domain	BinaryClassification
Learn time	7s
Train error	0.005
Predict train time	3s
Test error	0.047
Predict test time	2s

Log file

./run:3: warning: parenthesize argument(s) for future version
Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
MulticlassClassification
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
=== START program2: ./run learn ../program1/data
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program2: ./run learn ../program1/data --- OK [7s]
=== END program1: ./run learn ../dataset3/train --- OK [7s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [3s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s]

===== 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 program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [2s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m18.025s
user	0m7.052s
sys	0m0.592s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) binary-to-multi: Allows multiclass classification to be run on binary classification datasets (trivial reduction).
    • (multiclassLearner:Program[MulticlassClassification]) AttributeSelectedClassifier_weka_nominal: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/meta/AttributeSelectedClassifier.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
      ---------------------
      
      NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
      
      NOTE: WEKA's Classifiers read a data in the .arff format. For Multiclass datasets, the SVMlight format converted to .arff multiclass format so they can be read by WEKA programs
  • (dataset:Dataset) Simple Bio-info Glycosylation Data:
  • (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.0465116279069767
    • numErrors: 8
    • numExamples: 172
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 2
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.005
    • numErrors: 2
    • numExamples: 400
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 3
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 1
• exitCode: 0
• learn:
  • learn:
    • success: true
    • time: 7
  • success: true
  • time: 7
• success: true

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