DEVELOPMENT... OpenML
Run
50850

Run 50850

Task 1701 (Learning Curve) anneal Uploaded 06-11-2014 by Felicia West
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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
0.9843 ± 0.011
Per class
Build CPU time
0.049
Build memory
104058901.8667
F measure
0.8636 ± 0.0698
Per class
Kappa
0.6389 ± 0.1514
KB relative information score
0.5777 ± 0.0385
Mean absolute error
0.0806 ± 0.0061
Mean prior absolute error
0.1343 ± 0.0013
Number of instances
898
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8964 ± 0.0382
Per class
Predictive accuracy
0.8831 ± 0.0401
Prior entropy
1.1915 ± 0.026
Recall
0.8831 ± 0.0401
Per class
Relative absolute error
0.6002 ± 0.0427
Root mean prior squared error
0.2582 ± 0.0024
Root mean squared error
0.1673 ± 0.0125
Root relative squared error
0.648 ± 0.0457
SciMark benchmark
946.7958