Run
70612

Run 70612

Task 2354 (Supervised Classification) meta_ensembles.arff Uploaded 01-12-2014 by Joaquin Vanschoren
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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)_I40
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
0.8113
Per class
Cross-validation details (33% Holdout set)
Build CPU time
0.581
Cross-validation details (33% Holdout set)
Build memory
158676496
Cross-validation details (33% Holdout set)
F measure
0.5295
Per class
Cross-validation details (33% Holdout set)
Kappa
0.2105
Cross-validation details (33% Holdout set)
KB relative information score
8.8707
Cross-validation details (33% Holdout set)
Mean absolute error
0.2235
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.2983
Cross-validation details (33% Holdout set)
Number of instances
24
Per class
Cross-validation details (33% Holdout set)
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5046
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.5833
Cross-validation details (33% Holdout set)
Prior entropy
1.5899
Cross-validation details (33% Holdout set)
Recall
0.5833
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.749
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.3886
Cross-validation details (33% Holdout set)
Root mean squared error
0.3533
Cross-validation details (33% Holdout set)
Root relative squared error
0.9091
Cross-validation details (33% Holdout set)
SciMark benchmark
902.1764