DEVELOPMENT... OpenML
Run
55160

Run 55160

Task 1736 (Learning Curve) diabetes Uploaded 10-11-2014 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
0.7973 ± 0.0464
Per class
Build CPU time
0.011
Build memory
426103952.5
F measure
0.6907 ± 0.0624
Per class
Kappa
0.3049 ± 0.1352
KB relative information score
0.1805 ± 0.0409
Mean absolute error
0.38 ± 0.0149
Mean prior absolute error
0.4545 ± 0.0015
Number of instances
768
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.7178 ± 0.0522
Per class
Predictive accuracy
0.7214 ± 0.0434
Prior entropy
0.9331 ± 0.0046
Recall
0.7214 ± 0.0434
Per class
Relative absolute error
0.8361 ± 0.0321
Root mean prior squared error
0.4766 ± 0.0016
Root mean squared error
0.4228 ± 0.0138
Root relative squared error
0.8871 ± 0.0281
SciMark benchmark
1606.2579