DEVELOPMENT... OpenML
Run
50840

Run 50840

Task 2102 (Learning Curve) eucalyptus Uploaded 06-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.RandomSubSpace_REPTree(2)Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.
weka.REPTree(5)_I0.0
weka.REPTree(5)_L-1
weka.REPTree(5)_M2
weka.REPTree(5)_N3
weka.REPTree(5)_S1
weka.REPTree(5)_V0.001
weka.RandomSubSpace_REPTree(2)_P0.5
weka.RandomSubSpace_REPTree(2)_S1
weka.RandomSubSpace_REPTree(2)_num-slots1
weka.RandomSubSpace_REPTree(2)_I10
weka.RandomSubSpace_REPTree(2)_Wweka.classifiers.trees.REPTree

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
0.8403 ± 0.0258
Per class
Build CPU time
0.0109
Build memory
648511476.7
F measure
0.5415 ± 0.0635
Per class
Kappa
0.4202 ± 0.0798
KB relative information score
0.3873 ± 0.0264
Mean absolute error
0.2266 ± 0.0065
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
0.5493 ± 0.0679
Per class
Predictive accuracy
0.5557 ± 0.0613
Prior entropy
2.2621 ± 0.0043
Recall
0.5557 ± 0.0613
Per class
Relative absolute error
0.7235 ± 0.0207
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.335 ± 0.0102
Root relative squared error
0.8467 ± 0.0259
SciMark benchmark
1462.4645