Run
661

Run 661

Task 94 (Learning Curve) dermatology Uploaded 07-04-2014 by Jan van Rijn
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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.

17 Evaluation measures

Area under ROC curve
0.6748 ± 0.009
Per class
Kappa
0.3387 ± 0.0144
KB relative information score
0.3614 ± 0.012
Mean absolute error
0.1659 ± 0.0039
Mean prior absolute error
0.2664 ± 0.0004
Number of instances
3660
Per class
OS information
[ Sun Microsystems Inc., 1.6.0_30, amd64, Linux, 2.6.32-358.6.2.el6.x86_64 ]
Predictive accuracy
0.5022 ± 0.0117
Prior entropy
2.4327 ± 0.0089
Recall
0.5022 ± 0.0117
Per class
Relative absolute error
0.6228 ± 0.014
Root mean prior squared error
0.3649 ± 0.0006
Root mean squared error
0.4074 ± 0.0048
Root relative squared error
1.1164 ± 0.0119
SciMark benchmark
892.9577