DEVELOPMENT... OpenML
Run
69586

Run 69586

Task 3668 (Supervised Classification) delta_ailerons 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
0.9753
Per class
Build CPU time
2.2338
Build memory
313938717.9262
F measure
0.9423
Per class
Kappa
0.8841
KB relative information score
5805.7888
Mean absolute error
0.1037
Mean prior absolute error
0.4981
Number of instances
7129
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9426
Per class
Predictive accuracy
0.9423
Prior entropy
0.9973
Recall
0.9423
Per class
Relative absolute error
0.2081
Root mean prior squared error
0.4991
Root mean squared error
0.2207
Root relative squared error
0.4423
SciMark benchmark
934.625