Run
51629

Run 51629

Task 2102 (Learning Curve) eucalyptus Uploaded 06-11-2014 by Joaquin Vanschoren
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Flow

weka.MultiBoostAB_RandomTree(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.RandomTree(2)_K0
weka.RandomTree(2)_M1.0
weka.RandomTree(2)_S1
weka.RandomTree(2)_V0.001
weka.MultiBoostAB_RandomTree(2)_C3
weka.MultiBoostAB_RandomTree(2)_P100
weka.MultiBoostAB_RandomTree(2)_S1
weka.MultiBoostAB_RandomTree(2)_I10
weka.MultiBoostAB_RandomTree(2)_Wweka.classifiers.trees.RandomTree

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
0.709 ± 0.0256
Per class
Build CPU time
0.0044
Build memory
763078564.3
F measure
0.527 ± 0.0454
Per class
Kappa
0.3933 ± 0.0513
KB relative information score
0.4397 ± 0.0477
Mean absolute error
0.1905 ± 0.0175
Mean prior absolute error
0.3132 ± 0.0002
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5291 ± 0.0498
Per class
Predictive accuracy
0.5258 ± 0.0435
Prior entropy
2.2621 ± 0.0043
Recall
0.5258 ± 0.0435
Per class
Relative absolute error
0.6082 ± 0.0558
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.4281 ± 0.0188
Root relative squared error
1.0818 ± 0.0473
SciMark benchmark
948.877