DEVELOPMENT... OpenML
Run
26032

Run 26032

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

weka.RotationForest_PrincipalComponents_SMO_PolyKernel(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.SMO_PolyKernel(7)_C1.0
weka.SMO_PolyKernel(7)_L0.001
weka.SMO_PolyKernel(7)_N0
weka.SMO_PolyKernel(7)_P1.0E-12
weka.SMO_PolyKernel(7)_V-1
weka.SMO_PolyKernel(7)_W1
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_G3
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_H3
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_P50
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_Fweka.filters.unsupervised.attribute.PrincipalComponents
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_S1
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_num-slots1
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_I160
weka.RotationForest_PrincipalComponents_SMO_PolyKernel(2)_Wweka.classifiers.functions.SMO

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
1.1363
Build memory
481547704
F measure
0.9733
Per class
Kappa
0.96
KB relative information score
91.8495
Mean absolute error
0.2295
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
0.9733
Per class
Predictive accuracy
0.9733
Prior entropy
1.585
Recall
0.9733
Per class
Relative absolute error
0.5164
Root mean prior squared error
0.4714
Root mean squared error
0.2837
Root relative squared error
0.6018
SciMark benchmark
1684.5627[1030.87506356800871236.019877575262663.41787321353561358.25910427125654134.241798736152]