DEVELOPMENT... OpenML
Run
26045

Run 26045

Task 59 (Supervised Classification) iris Uploaded 07-10-2014 by Felicia West
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Flow

weka.RotationForest_PrincipalComponents_JRip(2)Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630. URL http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.211.
weka.JRip(2)_F3
weka.JRip(2)_N2.0
weka.JRip(2)_O2
weka.JRip(2)_S1
weka.PrincipalComponents(2)_A5
weka.PrincipalComponents(2)_M-1
weka.PrincipalComponents(2)_R1.0
weka.RotationForest_PrincipalComponents_JRip(2)_G3
weka.RotationForest_PrincipalComponents_JRip(2)_H3
weka.RotationForest_PrincipalComponents_JRip(2)_P50
weka.RotationForest_PrincipalComponents_JRip(2)_Fweka.filters.unsupervised.attribute.PrincipalComponents
weka.RotationForest_PrincipalComponents_JRip(2)_S1
weka.RotationForest_PrincipalComponents_JRip(2)_num-slots1
weka.RotationForest_PrincipalComponents_JRip(2)_I40
weka.RotationForest_PrincipalComponents_JRip(2)_Wweka.classifiers.rules.JRip

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
0.9977
Per class
Build CPU time
0.0796
Build memory
421209553.6
F measure
0.96
Per class
Kappa
0.94
KB relative information score
134.6843
Mean absolute error
0.0597
Mean prior absolute error
0.4444
Number of instances
150
Per class
OS information
[Oracle Corporation, 1.7.0_67, x86_64, Mac OS X, 10.9.5]
Precision
0.96
Per class
Predictive accuracy
0.96
Prior entropy
1.585
Recall
0.96
Per class
Relative absolute error
0.1343
Root mean prior squared error
0.4714
Root mean squared error
0.1358
Root relative squared error
0.288
SciMark benchmark
1684.5627[1030.87506356800871236.019877575262663.41787321353561358.25910427125654134.241798736152]