OpenML
435209

Run 435209

Task 64 (Learning Curve) labor Uploaded 12-05-2015 by Joaquin Vanschoren
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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
0.9345 ± 0.1127
Per class
F measure
0.8322 ± 0.1412
Per class
Kappa
0.6241 ± 0.3494
KB relative information score
0.3695 ± 0.1398
Mean absolute error
0.312 ± 0.0489
Mean prior absolute error
0.457 ± 0.0208
Number of instances
570
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8411 ± 0.1301
Per class
Predictive accuracy
0.8386 ± 0.1362
Prior entropy
0.9349 ± 0.0618
Recall
0.8386 ± 0.1362
Per class
Relative absolute error
0.6827 ± 0.1074
Root mean prior squared error
0.4773 ± 0.0219
Root mean squared error
0.3555 ± 0.0606
Root relative squared error
0.7448 ± 0.1279
SciMark benchmark
921.0859
UserCPU time millis
50
UserCPU time millis testing
10
UserCPU time millis training
40