Run
436296

Run 436296

Task 73 (Learning Curve) mfeat-fourier Uploaded 12-05-2015 by Joaquin Vanschoren
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Flow

weka.RotationForest_PrincipalComponents_J48(14)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(28)_C0.25
weka.J48(28)_M2
weka.PrincipalComponents(12)_R1.0
weka.PrincipalComponents(12)_A5
weka.PrincipalComponents(12)_M-1
weka.RotationForest_PrincipalComponents_J48(14)_G3
weka.RotationForest_PrincipalComponents_J48(14)_H3
weka.RotationForest_PrincipalComponents_J48(14)_P50
weka.RotationForest_PrincipalComponents_J48(14)_Fweka.filters.unsupervised.attribute.PrincipalComponents
weka.RotationForest_PrincipalComponents_J48(14)_S1
weka.RotationForest_PrincipalComponents_J48(14)_num-slots1
weka.RotationForest_PrincipalComponents_J48(14)_I40
weka.RotationForest_PrincipalComponents_J48(14)_Wweka.classifiers.trees.J48

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.

22 Evaluation measures

Area under ROC curve
0.9819 ± 0.0035
Per class
F measure
0.8331 ± 0.0227
Per class
Kappa
0.8153 ± 0.0251
KB relative information score
0.7754 ± 0.0126
Mean absolute error
0.0671 ± 0.0025
Mean prior absolute error
0.18
Number of instances
20000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8334 ± 0.0232
Per class
Predictive accuracy
0.8338 ± 0.0226
Prior entropy
3.3219
Recall
0.8338 ± 0.0226
Per class
Relative absolute error
0.3726 ± 0.0142
Root mean prior squared error
0.3
Root mean squared error
0.16 ± 0.0053
Root relative squared error
0.5333 ± 0.0178
SciMark benchmark
923.2201
UserCPU time millis
33430
UserCPU time millis testing
7150
UserCPU time millis training
26280