DEVELOPMENT... OpenML
Run
69695

Run 69695

Task 3798 (Supervised Classification) fri_c1_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
0.9112
Per class
Build CPU time
0.108
Build memory
373931906.4
F measure
0.8204
Per class
Kappa
0.6365
KB relative information score
96.5688
Mean absolute error
0.3235
Mean prior absolute error
0.4929
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
0.8214
Per class
Predictive accuracy
0.82
Prior entropy
0.9898
Recall
0.82
Per class
Relative absolute error
0.6563
Root mean prior squared error
0.4964
Root mean squared error
0.3688
Root relative squared error
0.743
SciMark benchmark
910.9043