DEVELOPMENT... OpenML
Run
50797

Run 50797

Task 2102 (Learning Curve) eucalyptus Uploaded 06-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)Weka implementation of IterativeClassifierOptimizer
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I10
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_L50
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_P1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_E1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_I1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_F10
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_R1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_Wweka.classifiers.meta.LogitBoost
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_metricRMSE
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_S1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

21 Evaluation measures

Area under ROC curve
0.8941 ± 0.0122
Per class
Build CPU time
0.194
Build memory
748664445.5
F measure
0.6593 ± 0.0405
Per class
Kappa
0.5639 ± 0.0503
KB relative information score
0.5291 ± 0.0292
Mean absolute error
0.1807 ± 0.0096
Mean prior absolute error
0.3132 ± 0.0002
Number of instances
736
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.6643 ± 0.0468
Per class
Predictive accuracy
0.6617 ± 0.0399
Prior entropy
2.2621 ± 0.0043
Recall
0.6617 ± 0.0399
Per class
Relative absolute error
0.577 ± 0.0306
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.3047 ± 0.0112
Root relative squared error
0.77 ± 0.0282
SciMark benchmark
1948.3582

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