DEVELOPMENT... OpenML
Run
59057

Run 59057

Task 2094 (Learning Curve) adult Uploaded 15-11-2014 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
0.9044 ± 0.0047
Per class
Build CPU time
0.3445
Build memory
2894143713.4
F measure
0.8363 ± 0.0046
Per class
Kappa
0.5285 ± 0.013
KB relative information score
0.3434 ± 0.0096
Mean absolute error
0.2361 ± 0.0022
Mean prior absolute error
0.3641 ± 0
Number of instances
48842
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.84 ± 0.0055
Per class
Predictive accuracy
0.8473 ± 0.0045
Prior entropy
0.7938 ± 0.0002
Recall
0.8473 ± 0.0045
Per class
Relative absolute error
0.6486 ± 0.0061
Root mean prior squared error
0.4266 ± 0.0001
Root mean squared error
0.3258 ± 0.0029
Root relative squared error
0.7637 ± 0.0068
SciMark benchmark
1769.7895