Run
68947

Run 68947

Task 287 (Supervised Classification) ionosphere Uploaded 30-11-2014 by Joaquin Vanschoren
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Flow

weka.RotationForest_PrincipalComponents_J48(3)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(3)_C0.25
weka.J48(3)_M2
weka.RotationForest_PrincipalComponents_J48(3)_G3
weka.RotationForest_PrincipalComponents_J48(3)_H3
weka.RotationForest_PrincipalComponents_J48(3)_I10
weka.RotationForest_PrincipalComponents_J48(3)_P50
weka.RotationForest_PrincipalComponents_J48(3)_S1
weka.RotationForest_PrincipalComponents_J48(3)_Wweka.classifiers.trees.J48
weka.RotationForest_PrincipalComponents_J48(3)_num-slots1

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.

21 Evaluation measures

Area under ROC curve
0.9764
Per class
Cross-validation details (33% Holdout set)
Build CPU time
0.148
Cross-validation details (33% Holdout set)
Build memory
130572504
Cross-validation details (33% Holdout set)
F measure
0.9652
Per class
Cross-validation details (33% Holdout set)
Kappa
0.9224
Cross-validation details (33% Holdout set)
KB relative information score
85.18
Cross-validation details (33% Holdout set)
Mean absolute error
0.1273
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4549
Cross-validation details (33% Holdout set)
Number of instances
115
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
0.9652
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9652
Cross-validation details (33% Holdout set)
Prior entropy
0.9425
Cross-validation details (33% Holdout set)
Recall
0.9652
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.2798
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4739
Cross-validation details (33% Holdout set)
Root mean squared error
0.2079
Cross-validation details (33% Holdout set)
Root relative squared error
0.4387
Cross-validation details (33% Holdout set)
SciMark benchmark
947.2295