DEVELOPMENT... OpenML
Run
48698

Run 48698

Task 1740 (Learning Curve) soybean 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
0.9758 ± 0.0128
Per class
Build CPU time
0.0067
Build memory
2229578532.2
F measure
0.9021 ± 0.0297
Per class
Kappa
0.8956 ± 0.0325
KB relative information score
0.907 ± 0.0185
Mean absolute error
0.0142 ± 0.0021
Mean prior absolute error
0.0961 ± 0.0001
Number of instances
683
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9064 ± 0.0293
Per class
Predictive accuracy
0.9048 ± 0.0297
Prior entropy
3.8358 ± 0.0227
Recall
0.9048 ± 0.0297
Per class
Relative absolute error
0.1476 ± 0.0218
Root mean prior squared error
0.2191 ± 0.0002
Root mean squared error
0.0912 ± 0.0137
Root relative squared error
0.416 ± 0.0623
SciMark benchmark
1723.761