DEVELOPMENT... OpenML
Run
26019

Run 26019

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

weka.RotationForest_PrincipalComponents_NaiveBayes(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.PrincipalComponents(2)_A5
weka.PrincipalComponents(2)_M-1
weka.PrincipalComponents(2)_R1.0
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_G3
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_H3
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_P50
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_Fweka.filters.unsupervised.attribute.PrincipalComponents
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_S1
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_num-slots1
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_I20
weka.RotationForest_PrincipalComponents_NaiveBayes(2)_Wweka.classifiers.bayes.NaiveBayes

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
0.9984
Per class
Build CPU time
0.0157
Build memory
271709388
F measure
0.9667
Per class
Kappa
0.95
KB relative information score
138.8367
Mean absolute error
0.0427
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
0.9668
Per class
Predictive accuracy
0.9667
Prior entropy
1.585
Recall
0.9667
Per class
Relative absolute error
0.096
Root mean prior squared error
0.4714
Root mean squared error
0.1226
Root relative squared error
0.2601
SciMark benchmark
1684.5627[1030.87506356800871236.019877575262663.41787321353561358.25910427125654134.241798736152]