OpenML
435247

Run 435247

Task 99 (Learning Curve) glass 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
0.9178 ± 0.0372
Per class
F measure
0.7084 ± 0.1082
Per class
Kappa
0.6309 ± 0.1217
KB relative information score
0.5231 ± 0.0522
Mean absolute error
0.1322 ± 0.0103
Mean prior absolute error
0.2116 ± 0.0025
Number of instances
2140
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6884 ± 0.0981
Per class
Predictive accuracy
0.7379 ± 0.086
Prior entropy
2.1835 ± 0.0748
Recall
0.7379 ± 0.086
Per class
Relative absolute error
0.625 ± 0.0465
Root mean prior squared error
0.3244 ± 0.0039
Root mean squared error
0.2366 ± 0.0178
Root relative squared error
0.7291 ± 0.0524
SciMark benchmark
942.2965
UserCPU time millis
180
UserCPU time millis testing
40
UserCPU time millis training
140