Run
436617

Run 436617

Task 74 (Learning Curve) breast-w 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)_I160
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
0.9912 ± 0.0075
Per class
F measure
0.9723 ± 0.0202
Per class
Kappa
0.9389 ± 0.044
KB relative information score
0.8906 ± 0.039
Mean absolute error
0.0547 ± 0.0164
Mean prior absolute error
0.4519 ± 0.0014
Number of instances
6990
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.973 ± 0.0187
Per class
Predictive accuracy
0.9721 ± 0.0205
Prior entropy
0.9293 ± 0.0041
Recall
0.9721 ± 0.0205
Per class
Relative absolute error
0.1211 ± 0.0363
Root mean prior squared error
0.4753 ± 0.0014
Root mean squared error
0.1555 ± 0.0479
Root relative squared error
0.3271 ± 0.1007
SciMark benchmark
900.9365
UserCPU time millis
2450
UserCPU time millis testing
1080
UserCPU time millis training
1370