DEVELOPMENT... OpenML
Run
53441

Run 53441

Task 1752 (Learning Curve) vehicle Uploaded 08-11-2014 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
0.9216 ± 0.0141
Per class
Build CPU time
0.0173
Build memory
1048886229.8667
F measure
0.6896 ± 0.0371
Per class
Kappa
0.6094 ± 0.0444
KB relative information score
0.5989 ± 0.0193
Mean absolute error
0.19 ± 0.0078
Mean prior absolute error
0.3748 ± 0
Number of instances
846
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.6841 ± 0.0424
Per class
Predictive accuracy
0.7069 ± 0.0334
Prior entropy
1.9991 ± 0.0005
Recall
0.7069 ± 0.0334
Per class
Relative absolute error
0.507 ± 0.0207
Root mean prior squared error
0.4329 ± 0
Root mean squared error
0.2902 ± 0.0107
Root relative squared error
0.6704 ± 0.0247
SciMark benchmark
1914.93