Run
436217

Run 436217

Task 70 (Learning Curve) balance-scale 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)_I80
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
0.9919 ± 0.0055
Per class
F measure
0.9101 ± 0.0302
Per class
Kappa
0.8591 ± 0.0449
KB relative information score
0.7663 ± 0.0378
Mean absolute error
0.0986 ± 0.0151
Mean prior absolute error
0.3798 ± 0.0012
Number of instances
6250
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9121 ± 0.0379
Per class
Predictive accuracy
0.9222 ± 0.0245
Prior entropy
1.3181 ± 0.0119
Recall
0.9222 ± 0.0245
Per class
Relative absolute error
0.2596 ± 0.0396
Root mean prior squared error
0.4356 ± 0.0014
Root mean squared error
0.193 ± 0.0191
Root relative squared error
0.4431 ± 0.0437
SciMark benchmark
895.2913
UserCPU time millis
770
UserCPU time millis testing
200
UserCPU time millis training
570