DEVELOPMENT... OpenML
Run
50853

Run 50853

Task 1714 (Learning Curve) breast-w Uploaded 06-11-2014 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
0.9902 ± 0.0086
Per class
Build CPU time
0.0049
Build memory
1779633019.1
F measure
0.96 ± 0.0229
Per class
Kappa
0.9117 ± 0.0501
KB relative information score
0.8375 ± 0.043
Mean absolute error
0.0826 ± 0.0184
Mean prior absolute error
0.4519 ± 0.0014
Number of instances
699
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9602 ± 0.0223
Per class
Predictive accuracy
0.9599 ± 0.0231
Prior entropy
0.9293 ± 0.0043
Recall
0.9599 ± 0.0231
Per class
Relative absolute error
0.1828 ± 0.0408
Root mean prior squared error
0.4753 ± 0.0015
Root mean squared error
0.1788 ± 0.0418
Root relative squared error
0.3762 ± 0.088
SciMark benchmark
1367.6268