Run
412095

Run 412095

Task 2354 (Supervised Classification) meta_ensembles.arff Uploaded 01-05-2015 by Joaquin Vanschoren
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.Decorate(2)P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003. P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems..
weka.Decorate(2)_E15
weka.Decorate(2)_R1.0
weka.Decorate(2)_S1
weka.Decorate(2)_I50

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
0.8134
Per class
Cross-validation details (33% Holdout set)
F measure
0.5619
Per class
Cross-validation details (33% Holdout set)
Kappa
0.2615
Cross-validation details (33% Holdout set)
KB relative information score
7.1675
Cross-validation details (33% Holdout set)
Mean absolute error
0.2374
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.2983
Cross-validation details (33% Holdout set)
Number of instances
24
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
0.5486
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.5833
Cross-validation details (33% Holdout set)
Prior entropy
1.5899
Cross-validation details (33% Holdout set)
Recall
0.5833
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.7956
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.3886
Cross-validation details (33% Holdout set)
Root mean squared error
0.3706
Cross-validation details (33% Holdout set)
Root relative squared error
0.9538
Cross-validation details (33% Holdout set)
SciMark benchmark
936.7184
UserCPU time millis
250
Cross-validation details (33% Holdout set)
UserCPU time millis training
250
Cross-validation details (33% Holdout set)