Run
435308

Run 435308

Task 2098 (Learning Curve) abalone Uploaded 12-05-2015 by Joaquin Vanschoren
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  • study_2 study_37 weka weka_3.7.12
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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.

20 Evaluation measures

Area under ROC curve
0.7684 ± 0.0089
Per class
Kappa
0.1651 ± 0.0207
KB relative information score
0.241 ± 0.0083
Mean absolute error
0.0564 ± 0.0002
Mean prior absolute error
0.0618 ± 0
Number of instances
4177
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Predictive accuracy
0.2717 ± 0.0177
Prior entropy
3.6031 ± 0.0188
Recall
0.2717 ± 0.0177
Per class
Relative absolute error
0.9123 ± 0.0041
Root mean prior squared error
0.1757 ± 0
Root mean squared error
0.1674 ± 0.0008
Root relative squared error
0.9529 ± 0.0044
SciMark benchmark
938.0833
UserCPU time millis
5900
UserCPU time millis testing
670
UserCPU time millis training
5230