MLcomp version alpha Statistics: 2084 users, 670 datasets, 390 programs, 18946 runs (0 queued, 0 running), 0 workers

Run 15774

Creator	morph
Program	RotationForest_weka_nominal
Dataset	CVExt2
Task type	MulticlassClassification
Created	1y231d ago
Download	Login required!

Status: Done!

Total Time	22m58s
Max Memory Usage	428M
Domain	BinaryClassification
Learn time
Train error	0.092
Predict train time
Test error	0.099
Predict test time

Log file

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
MulticlassClassification
=== END program2: ./run learn ../program1/data --- OK [1072s]
=== END program1: ./run learn ../dataset3/train --- OK [1077s]

===== 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 [5s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output
MulticlassClassification
=== END program2: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out.multiclass-output --- OK [191s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [192s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [6s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output
MulticlassClassification
=== END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [81s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [82s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [2s]


real	23m1.356s
user	21m59.138s
sys	0m24.354s

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]) RotationForest_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/RotationForest.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Class for construction a Rotation Forest. Can do classification and regression depending on the base learner. For more information, see Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.
      ---------------------
      
      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) CVExt2:
  • (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: 0.0985613153688057
    • numErrors: 2158
    • numExamples: 21895
    • success: true
    • time: 2
  • predict:
    • predict:
      • success: true
      • time: 81
    • success: true
    • time: 82
  • strip:
    • success: true
    • time: 2
• doTrain:
  • evaluate:
    • errorRate: 0.0916653617287817
    • numErrors: 4683
    • numExamples: 51088
    • success: true
    • time: 6
  • predict:
    • predict:
      • success: true
      • time: 191
    • success: true
    • time: 192
  • strip:
    • success: true
    • time: 5
• exitCode: 0
• learn:
  • learn:
    • success: true
    • time: 1072
  • success: true
  • time: 1077
• success: true

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