MLcomp version alpha Statistics: 2051 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 27190

Creator	favre
Program	LogitBoost_weka_nominal
Dataset	cemst-auditor-performance-prediction-asr1
Task type	MulticlassClassification
Created	298d16h ago
Download	Login required!

Status: Done!

Total Time	24m59s
Max Memory Usage	419M
Domain	MulticlassClassification
Learn time	23m17s
Train error	0.273
Predict train time	1m14s
Test error	0.265
Predict test time	21s

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 ../dataset2/train
MulticlassClassification
=== END program1: ./run learn ../dataset2/train --- OK [1397s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
MulticlassClassification
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [74s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [3s]

===== 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
MulticlassClassification
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [21s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]


real	25m1.191s
user	14m38.315s
sys	0m4.104s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) 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) cemst-auditor-performance-prediction-asr1: Complementary Evaluation Measures for Speech Transcription, train-test split for task auditor-performance-prediction with asr1
  • (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: 0.264824120603015
    • numErrors: 3162
    • numExamples: 11940
    • success: true
    • time: 1
  • predict:
    • success: true
    • time: 21
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • errorRate: 0.272954175781031
    • numErrors: 9733
    • numExamples: 35658
    • success: true
    • time: 3
  • predict:
    • success: true
    • time: 74
  • strip:
    • success: true
    • time: 1
• exitCode: 0
• learn:
  • success: true
  • time: 1397
• success: true

Comments: