Run
431108

Run 431108

Task 264 (Supervised Classification) postoperative-patient-data Uploaded 11-05-2015 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)_I20
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
0.5311
Per class
Cross-validation details (33% Holdout set)
F measure
0.6083
Per class
Cross-validation details (33% Holdout set)
KB relative information score
-1.046
Cross-validation details (33% Holdout set)
Mean absolute error
0.282
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.2798
Cross-validation details (33% Holdout set)
Number of instances
29
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
0.5244
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7241
Cross-validation details (33% Holdout set)
Prior entropy
1.0305
Cross-validation details (33% Holdout set)
Recall
0.7241
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
1.0079
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.3657
Cross-validation details (33% Holdout set)
Root mean squared error
0.3731
Cross-validation details (33% Holdout set)
Root relative squared error
1.0201
Cross-validation details (33% Holdout set)
SciMark benchmark
932.4744
UserCPU time millis
80
Cross-validation details (33% Holdout set)
UserCPU time millis testing
20
Cross-validation details (33% Holdout set)
UserCPU time millis training
60
Cross-validation details (33% Holdout set)