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

Run 13579

Creator	yorinasub17
Program	AdaBoostM1_weka_nominal
Dataset	Synthetic 75% Density, Large, Few Labels
Task type	MulticlassClassification
Created	28d17h ago
Download	Login required!

Status: Done!

Total Time	14m48s
Max Memory Usage	421M
Domain	MulticlassClassification
Learn time	7m47s
Train error	0.497
Predict train time	5m9s
Test error	0.536
Predict test time	1m37s

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 ../dataset2/train
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [467s]

===== 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 [5s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [309s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [5s]

===== 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 [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
./run:3: warning: parenthesize argument(s) for future version
MulticlassClassification
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [97s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s]


real	14m50.829s
user	5m23.052s
sys	0m10.433s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) AdaBoostM1_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/AdaBoostM1.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits. For more information, see Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
    ---------------------
    
    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) Synthetic 75% Density, Large, Few Labels : A synthetically generated data set Attributes: =label(i) = argmax_j w(j)'*x(i) for randomly generated weight vectors. =weight vectors elements are independently sampled from Normal distribution. =density is what percentage of weight vector's elements were not set to 0 =x(i) normally distributed according to multivariate Gaussian, random parameters
  • (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).

Results

• doTest:
  • evaluate:
    • errorRate: 0.536
    • numErrors: 1206
    • numExamples: 2250
    • success: true
    • time: 2
  • predict:
    • success: true
    • time: 97
  • strip:
    • success: true
    • time: 2
• doTrain:
  • evaluate:
    • errorRate: 0.496571428571429
    • numErrors: 2607
    • numExamples: 5250
    • success: true
    • time: 5
  • predict:
    • success: true
    • time: 309
  • strip:
    • success: true
    • time: 5
• exitCode: 0
• learn:
  • success: true
  • time: 467
• success: true

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