DEVELOPMENT... OpenML
Run
61525

Run 61525

Task 2102 (Learning Curve) eucalyptus Uploaded 16-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.MultiBoostAB_J48(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
weka.J48(3)_C0.25
weka.J48(3)_M2
weka.MultiBoostAB_J48(2)_C3
weka.MultiBoostAB_J48(2)_P100
weka.MultiBoostAB_J48(2)_S1
weka.MultiBoostAB_J48(2)_I10
weka.MultiBoostAB_J48(2)_Wweka.classifiers.trees.J48

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
0.8625 ± 0.0252
Per class
Build CPU time
0.0429
Build memory
2367844124
F measure
0.6558 ± 0.0442
Per class
Kappa
0.5593 ± 0.0541
KB relative information score
0.5924 ± 0.0448
Mean absolute error
0.1397 ± 0.0154
Mean prior absolute error
0.3132 ± 0.0002
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.6599 ± 0.0454
Per class
Predictive accuracy
0.6576 ± 0.042
Prior entropy
2.2621 ± 0.0043
Recall
0.6576 ± 0.042
Per class
Relative absolute error
0.446 ± 0.0489
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.3526 ± 0.0194
Root relative squared error
0.891 ± 0.0488
SciMark benchmark
1707.2836

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