DEVELOPMENT... OpenML
Run
69072

Run 69072

Task 57 (Supervised Classification) ionosphere Uploaded 30-11-2014 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)_I10
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
0.9715
Per class
Build CPU time
0.3158
Build memory
256961360.661
F measure
0.9282
Per class
Kappa
0.8428
KB relative information score
263.6374
Mean absolute error
0.1236
Mean prior absolute error
0.4604
Number of instances
351
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9289
Per class
Predictive accuracy
0.9288
Prior entropy
0.9425
Recall
0.9288
Per class
Relative absolute error
0.2684
Root mean prior squared error
0.4797
Root mean squared error
0.2303
Root relative squared error
0.48
SciMark benchmark
943.443