DEVELOPMENT... OpenML
Run
50847

Run 50847

Task 1710 (Learning Curve) balance-scale 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.

19 Evaluation measures

Area under ROC curve
0.9068 ± 0.026
Per class
Build CPU time
0.0031
Build memory
374047335.5
Kappa
0.6677 ± 0.082
KB relative information score
0.2514 ± 0.0168
Mean absolute error
0.2951 ± 0.0063
Mean prior absolute error
0.3798 ± 0.0012
Number of instances
625
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Predictive accuracy
0.8208 ± 0.0444
Prior entropy
1.3181 ± 0.0124
Recall
0.8208 ± 0.0444
Per class
Relative absolute error
0.777 ± 0.0158
Root mean prior squared error
0.4356 ± 0.0014
Root mean squared error
0.3538 ± 0.007
Root relative squared error
0.8122 ± 0.015
SciMark benchmark
1462.4645