OpenML
435222

Run 435222

Task 74 (Learning Curve) breast-w 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
0.9893 ± 0.0087
Per class
F measure
0.9596 ± 0.0225
Per class
Kappa
0.9109 ± 0.0496
KB relative information score
0.8339 ± 0.0397
Mean absolute error
0.085 ± 0.0166
Mean prior absolute error
0.4519 ± 0.0014
Number of instances
6990
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9599 ± 0.0218
Per class
Predictive accuracy
0.9595 ± 0.0225
Prior entropy
0.9293 ± 0.0041
Recall
0.9595 ± 0.0225
Per class
Relative absolute error
0.188 ± 0.0366
Root mean prior squared error
0.4753 ± 0.0014
Root mean squared error
0.1775 ± 0.039
Root relative squared error
0.3736 ± 0.0819
SciMark benchmark
913.9348
UserCPU time millis
270
UserCPU time millis testing
60
UserCPU time millis training
210