Run
435306

Run 435306

Task 2102 (Learning Curve) eucalyptus Uploaded 12-05-2015 by Joaquin Vanschoren
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  • study_2 study_37 weka weka_3.7.12
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Flow

weka.RandomSubSpace_REPTree(4)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(9)_M2
weka.REPTree(9)_V0.001
weka.REPTree(9)_N3
weka.REPTree(9)_S1
weka.REPTree(9)_L-1
weka.REPTree(9)_I0.0
weka.RandomSubSpace_REPTree(4)_P0.5
weka.RandomSubSpace_REPTree(4)_S1
weka.RandomSubSpace_REPTree(4)_num-slots1
weka.RandomSubSpace_REPTree(4)_I160
weka.RandomSubSpace_REPTree(4)_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.

22 Evaluation measures

Area under ROC curve
0.8561 ± 0.0255
Per class
F measure
0.5667 ± 0.0659
Per class
Kappa
0.4516 ± 0.0817
KB relative information score
0.3948 ± 0.029
Mean absolute error
0.2256 ± 0.0073
Mean prior absolute error
0.3132 ± 0.0002
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5806 ± 0.0803
Per class
Predictive accuracy
0.5802 ± 0.0628
Prior entropy
2.2621 ± 0.0043
Recall
0.5802 ± 0.0628
Per class
Relative absolute error
0.7203 ± 0.0233
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.3285 ± 0.0098
Root relative squared error
0.8301 ± 0.0247
SciMark benchmark
903.7946
UserCPU time millis
700
UserCPU time millis testing
70
UserCPU time millis training
630