OpenML
311293

Run 311293

Task 64 (Learning Curve) labor Uploaded 20-03-2015 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)_I40
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
0.9302 ± 0.1195
Per class
F measure
0.8282 ± 0.1368
Per class
Kappa
0.615 ± 0.356
KB relative information score
0.367 ± 0.147
Mean absolute error
0.3126 ± 0.0525
Mean prior absolute error
0.457 ± 0.0208
Number of instances
570
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.8379 ± 0.1266
Per class
Predictive accuracy
0.8351 ± 0.1353
Prior entropy
0.9349 ± 0.0618
Recall
0.8351 ± 0.1353
Per class
Relative absolute error
0.6841 ± 0.1151
Root mean prior squared error
0.4773 ± 0.0219
Root mean squared error
0.3572 ± 0.0642
Root relative squared error
0.7484 ± 0.1355
SciMark benchmark
1323.4784
UserCPU time millis
10
UserCPU time millis training
10