DEVELOPMENT... OpenML
Run
15090

Run 15090

Task 59 (Supervised Classification) iris Uploaded 03-06-2014 by Felicia West
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Flow

weka.RotationForest_PrincipalComponents_RandomForest(1)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(1)_A5
weka.PrincipalComponents(1)_M-1
weka.PrincipalComponents(1)_R1.0
weka.RandomForest(1)_I10
weka.RandomForest(1)_K0
weka.RandomForest(1)_S1
weka.RandomForest(1)_num-slots1
weka.RotationForest_PrincipalComponents_RandomForest(1)_Fweka.filters.unsupervised.attribute.PrincipalComponents
weka.RotationForest_PrincipalComponents_RandomForest(1)_G3
weka.RotationForest_PrincipalComponents_RandomForest(1)_H3
weka.RotationForest_PrincipalComponents_RandomForest(1)_I160
weka.RotationForest_PrincipalComponents_RandomForest(1)_P50
weka.RotationForest_PrincipalComponents_RandomForest(1)_S1
weka.RotationForest_PrincipalComponents_RandomForest(1)_Wweka.classifiers.trees.RandomForest
weka.RotationForest_PrincipalComponents_RandomForest(1)_num-slots1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.9981
Per class
F measure
0.96
Per class
Kappa
0.94
KB relative information score
137.8842
Mean absolute error
0.0458
Mean prior absolute error
0.4444
Number of instances
150
Per class
Precision
0.96
Per class
Predictive accuracy
0.96
Prior entropy
1.585
Recall
0.96
Per class
Relative absolute error
0.103
Root mean prior squared error
0.4714
Root mean squared error
0.1282
Root relative squared error
0.272