MLcomp version alpha Statistics: 2040 users, 645 datasets, 389 programs, 18768 runs (0 queued, 3 running), 1 workers

Run 21049

Creator	internal
Program	LogitBoost_weka_nominal
Dataset	ralign
Task type	MulticlassClassification
Created	318d17h ago

Status: Done!

Total Time
Max Memory Usage
Domain	BinaryClassification
Learn time	3m52s
Train error	0.008
Predict train time	47s
Test error	0.008
Predict test time	19s

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 [228s]
=== END program1: ./run learn ../dataset3/train --- OK [232s]

===== 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 [44s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [47s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [7s]

===== 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 [18s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [19s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [3s]


real	5m26.178s
user	5m7.487s
sys	0m6.008s

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]) LogitBoost_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/LogitBoost.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
      
      ---------------------
      Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University. Can do efficient internal cross-validation to determine appropriate number of iterations.
      ---------------------
      
      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) ralign: an alignment problem
  • (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: 0.00774843417059469
    • numErrors: 360
    • numExamples: 46461
    • success: true
    • time: 3
  • predict:
    • predict:
      • success: true
      • time: 18
    • success: true
    • time: 19
  • strip:
    • success: true
    • time: 2
• doTrain:
  • evaluate:
    • errorRate: 0.00797911593240351
    • numErrors: 865
    • numExamples: 108408
    • success: true
    • time: 7
  • predict:
    • predict:
      • success: true
      • time: 44
    • success: true
    • time: 47
  • strip:
    • success: true
    • time: 5
• exitCode: 0
• learn:
  • learn:
    • success: true
    • time: 228
  • success: true
  • time: 232
• success: true

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