Run
64029

Run 64029

Task 3810 (Supervised Classification) arsenic-male-bladder Uploaded 17-11-2014 by Joaquin Vanschoren
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Flow

weka.MultiBoostAB_SPegasos(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.SPegasos(2)_E500
weka.SPegasos(2)_F0
weka.SPegasos(2)_L1.0E-4
weka.MultiBoostAB_SPegasos(2)_C3
weka.MultiBoostAB_SPegasos(2)_P100
weka.MultiBoostAB_SPegasos(2)_S1
weka.MultiBoostAB_SPegasos(2)_I80
weka.MultiBoostAB_SPegasos(2)_Wweka.classifiers.functions.SPegasos

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
0.8147
Per class
Cross-validation details (10-fold Crossvalidation)
Build CPU time
3.7543
Cross-validation details (10-fold Crossvalidation)
Build memory
579462637.4669
Cross-validation details (10-fold Crossvalidation)
F measure
0.9742
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6553
Cross-validation details (10-fold Crossvalidation)
KB relative information score
68.7396
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0351
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0837
Cross-validation details (10-fold Crossvalidation)
Number of instances
559
Per class
Cross-validation details (10-fold Crossvalidation)
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9749
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9767
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.2628
Cross-validation details (10-fold Crossvalidation)
Recall
0.9767
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4198
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1568
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7734
Cross-validation details (10-fold Crossvalidation)
SciMark benchmark
939.9473