DEVELOPMENT... OpenML
Run
870

Run 870

Task 116 (Learning Curve) ionosphere Uploaded 08-04-2014 by Mandy
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Flow

weka.OneR(1)R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
weka.OneR(1)_B6

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

19 Evaluation measures

Area under ROC curve
0.7946 ± 0.0607
Per class
F measure
0.8147 ± 0.056
Per class
Kappa
0.5953 ± 0.1211
KB relative information score
0.5854 ± 0.125
Mean absolute error
0.1843 ± 0.0557
Mean prior absolute error
0.4604 ± 0.0038
Number of instances
3510
Per class
OS information
[ Oracle Corporation, 1.7.0_15, amd64, Linux, 3.5.0-27-generic ]
Precision
0.8141 ± 0.0561
Per class
Predictive accuracy
0.8157 ± 0.0557
Prior entropy
0.9418 ± 0.0113
Recall
0.8157 ± 0.0557
Per class
Relative absolute error
0.4003 ± 0.1207
Root mean prior squared error
0.4797 ± 0.004
Root mean squared error
0.4293 ± 0.0661
Root relative squared error
0.895 ± 0.1377
SciMark benchmark
890.5946