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

Run 14689

Creator	internal
Program	Vote_weka_numeric
Dataset	simple-regression-test
Task type	Regression
Created	28d17h ago
Download	Login required!

Status: Done!

Total Time	35s
Max Memory Usage	416M
Domain	Regression
Learn time	4s
Train MSE	897
Predict train time	5s
Test MSE	882
Predict test time	0s

Log file

Note: Some input files use unchecked or unsafe operations.
Note: Recompile with -Xlint:unchecked for details.
Regression
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
Regression
=== END program1: ./run learn ../dataset2/train --- OK [4s]

===== 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 [8s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Regression
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [5s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [19s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Regression
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	0m41.641s
user	0m27.418s
sys	0m1.556s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) Vote_weka_numeric: This programs is part of the WEKA classifier library. The code used to generate this program is from the java class 'weka/classifiers/meta/Vote.java' from WEKA's libraries. The following description was taken from this classes JavaDoc information:
    
    ---------------------
    Class for combining classifiers. Different combinations of probability estimates for classification are available. For more information see: Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(3):226-239.
    ---------------------
    
    NOTE: This algorithm has no parameter tuning, it is using the default WEKA parameters
    
    
  • (dataset:Dataset) simple-regression-test: a linear function of one variable
  • (stripper:Program[Strip]) regression-utils: Validates and inspects a dataset in Regression format.
  • (evaluator:Program[Evaluate]) regression-evaluator: Evaluates predictions of Regression datasets (continuous outputs).

Results

• doTest:
  • evaluate:
    • meanSquaredError: 881.627007268043
    • numExamples: 10000
    • success: true
    • time: 1
    • totalMeanSquaredError: 8816270.07268043
  • predict:
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • meanSquaredError: 896.800709920163
    • numExamples: 300000
    • success: true
    • time: 19
    • totalMeanSquaredError: 269040212.976049
  • predict:
    • success: true
    • time: 5
  • strip:
    • success: true
    • time: 8
• exitCode: 0
• learn:
  • success: true
  • time: 4
• success: true

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