Run
20190

Run 20190

Task 27 (Supervised Classification) colic Uploaded 18-06-2014 by Joaquin Vanschoren
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Flow

weka.MultiBoostAB_BFTree(1)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.BFTree(1)_Ctrue
weka.BFTree(1)_M2
weka.BFTree(1)_N5
weka.BFTree(1)_PPOSTPRUNED
weka.BFTree(1)_S1
weka.MultiBoostAB_BFTree(1)_C3
weka.MultiBoostAB_BFTree(1)_I40
weka.MultiBoostAB_BFTree(1)_P100
weka.MultiBoostAB_BFTree(1)_S1
weka.MultiBoostAB_BFTree(1)_Wweka.classifiers.trees.BFTree

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

19 Evaluation measures

Area under ROC curve
0.8686
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8356
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6447
Cross-validation details (10-fold Crossvalidation)
KB relative information score
235.3003
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1631
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4662
Cross-validation details (10-fold Crossvalidation)
Number of instances
368
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
0.8355
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.837
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9509
Cross-validation details (10-fold Crossvalidation)
Recall
0.837
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3499
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4827
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3948
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8178
Cross-validation details (10-fold Crossvalidation)
SciMark benchmark
903.7939[560.12864292244511009.4054313942349480.4213690634573905.5060508708661563.5079546575598]