OpenML
438150

Run 438150

Task 2114 (Supervised Classification) solar-flare Uploaded 14-05-2015 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
0.5
Per class
Cross-validation details (33% Holdout set)
F measure
0.9957
Per class
Cross-validation details (33% Holdout set)
KB relative information score
7.7358
Cross-validation details (33% Holdout set)
Mean absolute error
0.0056
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.0062
Cross-validation details (33% Holdout set)
Number of instances
351
Per class
Cross-validation details (33% Holdout set)
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9943
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9972
Cross-validation details (33% Holdout set)
Prior entropy
0.0626
Cross-validation details (33% Holdout set)
Recall
0.9972
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.8986
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.0436
Cross-validation details (33% Holdout set)
Root mean squared error
0.0436
Cross-validation details (33% Holdout set)
Root relative squared error
0.9992
Cross-validation details (33% Holdout set)
SciMark benchmark
927.2882
UserCPU time millis
7220
Cross-validation details (33% Holdout set)
UserCPU time millis testing
3280
Cross-validation details (33% Holdout set)
UserCPU time millis training
3940
Cross-validation details (33% Holdout set)