DEVELOPMENT... OpenML
Run
70097

Run 70097

Task 250 (Supervised Classification) mfeat-pixel 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
0.9988
Per class
Build CPU time
210.937
Build memory
1168299176
F measure
0.9773
Per class
Kappa
0.9747
KB relative information score
595.7648
Mean absolute error
0.0332
Mean prior absolute error
0.18
Number of instances
660
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9774
Per class
Predictive accuracy
0.9773
Prior entropy
3.3219
Recall
0.9773
Per class
Relative absolute error
0.1845
Root mean prior squared error
0.3
Root mean squared error
0.0929
Root relative squared error
0.3096
SciMark benchmark
942.3187