DEVELOPMENT... OpenML
Run
48697

Run 48697

Task 1736 (Learning Curve) diabetes Uploaded 05-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.PART(2)Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
weka.PART(2)_C0.25
weka.PART(2)_M2
weka.PART(2)_Q1

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
0.7893 ± 0.0542
Per class
Build CPU time
0.0022
Build memory
3595609136
F measure
0.73 ± 0.0573
Per class
Kappa
0.4024 ± 0.1113
KB relative information score
0.3003 ± 0.0662
Mean absolute error
0.3172 ± 0.0257
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
0.7288 ± 0.042
Per class
Predictive accuracy
0.7318 ± 0.0575
Prior entropy
0.9331 ± 0.0046
Recall
0.7318 ± 0.0575
Per class
Relative absolute error
0.698 ± 0.0556
Root mean prior squared error
0.4766 ± 0.0016
Root mean squared error
0.4225 ± 0.0292
Root relative squared error
0.8865 ± 0.0601
SciMark benchmark
1729.8248