DEVELOPMENT... OpenML
Run
69547

Run 69547

Task 3658 (Supervised Classification) fri_c3_250_10 Uploaded 01-12-2014 by Felicia West
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Flow

weka.RotationForest_PrincipalComponents_J48(3)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.J48(3)_C0.25
weka.J48(3)_M2
weka.RotationForest_PrincipalComponents_J48(3)_G3
weka.RotationForest_PrincipalComponents_J48(3)_H3
weka.RotationForest_PrincipalComponents_J48(3)_I20
weka.RotationForest_PrincipalComponents_J48(3)_P50
weka.RotationForest_PrincipalComponents_J48(3)_S1
weka.RotationForest_PrincipalComponents_J48(3)_Wweka.classifiers.trees.J48
weka.RotationForest_PrincipalComponents_J48(3)_num-slots1

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
0.9328
Per class
Build CPU time
0.1097
Build memory
441287905.6
F measure
0.884
Per class
Kappa
0.7664
KB relative information score
117.1729
Mean absolute error
0.2894
Mean prior absolute error
0.4968
Number of instances
250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.884
Per class
Predictive accuracy
0.884
Prior entropy
0.9955
Recall
0.884
Per class
Relative absolute error
0.5826
Root mean prior squared error
0.4984
Root mean squared error
0.3382
Root relative squared error
0.6787
SciMark benchmark
943.443