OpenML
435134

Run 435134

Task 82 (Learning Curve) cmc 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)_I80
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
0.7288 ± 0.0277
Per class
F measure
0.5304 ± 0.0348
Per class
Kappa
0.2717 ± 0.0536
KB relative information score
0.1609 ± 0.0157
Mean absolute error
0.3877 ± 0.0056
Mean prior absolute error
0.4308 ± 0.0004
Number of instances
14730
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5332 ± 0.0377
Per class
Predictive accuracy
0.5419 ± 0.0337
Prior entropy
1.539 ± 0.0028
Recall
0.5419 ± 0.0337
Per class
Relative absolute error
0.8999 ± 0.0129
Root mean prior squared error
0.4641 ± 0.0004
Root mean squared error
0.432 ± 0.0057
Root relative squared error
0.9309 ± 0.0122
SciMark benchmark
903.7946
UserCPU time millis
430
UserCPU time millis testing
90
UserCPU time millis training
340