DEVELOPMENT... OpenML
Run
55074

Run 55074

Task 1719 (Learning Curve) mfeat-pixel Uploaded 10-11-2014 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 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
0.9938 ± 0.0041
Per class
Build CPU time
3.4902
Build memory
1172719190.1091
F measure
0.924 ± 0.0173
Per class
Kappa
0.9156 ± 0.0194
KB relative information score
0.8999 ± 0.0122
Mean absolute error
0.0287 ± 0.0029
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.9244 ± 0.0174
Per class
Predictive accuracy
0.924 ± 0.0174
Prior entropy
3.3219
Recall
0.924 ± 0.0174
Per class
Relative absolute error
0.1596 ± 0.0159
Root mean prior squared error
0.3
Root mean squared error
0.1072 ± 0.0098
Root relative squared error
0.3574 ± 0.0326
SciMark benchmark
1853.1129