DEVELOPMENT... OpenML
Run
26020

Run 26020

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)_I40
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
0.9985
Per class
Build CPU time
0.0337
Build memory
293016196.8
F measure
0.9733
Per class
Kappa
0.96
KB relative information score
140.8189
Mean absolute error
0.0349
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
0.9738
Per class
Predictive accuracy
0.9733
Prior entropy
1.585
Recall
0.9733
Per class
Relative absolute error
0.0786
Root mean prior squared error
0.4714
Root mean squared error
0.1135
Root relative squared error
0.2408
SciMark benchmark
1684.5627[1030.87506356800871236.019877575262663.41787321353561358.25910427125654134.241798736152]