Run
39496

Run 39496

Task 4517 (Supervised Classification) arsenic-female-bladder Uploaded 28-10-2014 by Joaquin Vanschoren
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Flow

weka.MultiBoostAB_ADTree(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.ADTree(2)_B10
weka.ADTree(2)_E-3
weka.ADTree(2)_S1
weka.MultiBoostAB_ADTree(2)_C3
weka.MultiBoostAB_ADTree(2)_P100
weka.MultiBoostAB_ADTree(2)_S1
weka.MultiBoostAB_ADTree(2)_I40
weka.MultiBoostAB_ADTree(2)_Wweka.classifiers.trees.ADTree

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
0.7582
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Build CPU time
0.3601
Cross-validation details (10 times 10-fold Crossvalidation)
Build memory
518329204.1603
Cross-validation details (10 times 10-fold Crossvalidation)
F measure
0.8373
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Kappa
0.2624
Cross-validation details (10 times 10-fold Crossvalidation)
KB relative information score
1598.237
Cross-validation details (10 times 10-fold Crossvalidation)
Mean absolute error
0.1393
Cross-validation details (10 times 10-fold Crossvalidation)
Mean prior absolute error
0.2462
Cross-validation details (10 times 10-fold Crossvalidation)
Number of instances
5590
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8331
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Predictive accuracy
0.861
Cross-validation details (10 times 10-fold Crossvalidation)
Prior entropy
0.5956
Cross-validation details (10 times 10-fold Crossvalidation)
Recall
0.861
Per class
Cross-validation details (10 times 10-fold Crossvalidation)
Relative absolute error
0.566
Cross-validation details (10 times 10-fold Crossvalidation)
Root mean prior squared error
0.3502
Cross-validation details (10 times 10-fold Crossvalidation)
Root mean squared error
0.3598
Cross-validation details (10 times 10-fold Crossvalidation)
Root relative squared error
1.0276
Cross-validation details (10 times 10-fold Crossvalidation)
SciMark benchmark
944.0892