DEVELOPMENT... OpenML
Run
50814

Run 50814

Task 1740 (Learning Curve) soybean 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
0.9958 ± 0.002
Per class
Build CPU time
0.5601
Build memory
5169923621.2
F measure
0.9393 ± 0.0207
Per class
Kappa
0.9342 ± 0.022
KB relative information score
0.9439 ± 0.0125
Mean absolute error
0.0097 ± 0.0017
Mean prior absolute error
0.0961 ± 0.0001
Number of instances
683
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.941 ± 0.0178
Per class
Predictive accuracy
0.94 ± 0.0201
Prior entropy
3.8358 ± 0.0227
Recall
0.94 ± 0.0201
Per class
Relative absolute error
0.1009 ± 0.0175
Root mean prior squared error
0.2191 ± 0.0002
Root mean squared error
0.0668 ± 0.0084
Root relative squared error
0.305 ± 0.0385
SciMark benchmark
1680.7345