Run
436585

Run 436585

Task 69 (Learning Curve) lymph Uploaded 12-05-2015 by Joaquin Vanschoren
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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
0.9364 ± 0.0628
Per class
F measure
0.8407 ± 0.0863
Per class
Kappa
0.7031 ± 0.1729
KB relative information score
0.5711 ± 0.1087
Mean absolute error
0.1315 ± 0.0287
Mean prior absolute error
0.2677 ± 0.0075
Number of instances
1480
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.837 ± 0.0847
Per class
Predictive accuracy
0.8459 ± 0.09
Prior entropy
1.2302 ± 0.1357
Recall
0.8459 ± 0.09
Per class
Relative absolute error
0.4913 ± 0.1036
Root mean prior squared error
0.3639 ± 0.0103
Root mean squared error
0.2397 ± 0.0472
Root relative squared error
0.6586 ± 0.1252
SciMark benchmark
935.5676
UserCPU time millis
1950
UserCPU time millis testing
910
UserCPU time millis training
1040