Run
435220

Run 435220

Task 72 (Learning Curve) breast-cancer 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
0.6726 ± 0.0943
Per class
F measure
0.6096 ± 0.0535
Per class
Kappa
0.0427 ± 0.0989
KB relative information score
0.0539 ± 0.0545
Mean absolute error
0.3932 ± 0.0167
Mean prior absolute error
0.4183 ± 0.0065
Number of instances
2860
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6323 ± 0.1261
Per class
Predictive accuracy
0.6983 ± 0.0338
Prior entropy
0.8779 ± 0.0199
Recall
0.6983 ± 0.0338
Per class
Relative absolute error
0.9399 ± 0.0354
Root mean prior squared error
0.457 ± 0.0071
Root mean squared error
0.4408 ± 0.0184
Root relative squared error
0.9644 ± 0.0353
SciMark benchmark
904.333
UserCPU time millis
140
UserCPU time millis testing
30
UserCPU time millis training
110