DEVELOPMENT... OpenML
Run
55164

Run 55164

Task 1742 (Learning Curve) spambase Uploaded 10-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.RandomSubSpace_REPTree(2)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(5)_I0.0
weka.REPTree(5)_L-1
weka.REPTree(5)_M2
weka.REPTree(5)_N3
weka.REPTree(5)_S1
weka.REPTree(5)_V0.001
weka.RandomSubSpace_REPTree(2)_P0.5
weka.RandomSubSpace_REPTree(2)_S1
weka.RandomSubSpace_REPTree(2)_num-slots1
weka.RandomSubSpace_REPTree(2)_I10
weka.RandomSubSpace_REPTree(2)_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.

21 Evaluation measures

Area under ROC curve
0.9802 ± 0.0056
Per class
Build CPU time
0.1932
Build memory
698006785.6571
F measure
0.9389 ± 0.0118
Per class
Kappa
0.8718 ± 0.0247
KB relative information score
0.7393 ± 0.0165
Mean absolute error
0.1407 ± 0.0071
Mean prior absolute error
0.4776 ± 0.0002
Number of instances
4601
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.9391 ± 0.012
Per class
Predictive accuracy
0.9391 ± 0.0118
Prior entropy
0.9674 ± 0.0006
Recall
0.9391 ± 0.0118
Per class
Relative absolute error
0.2946 ± 0.0149
Root mean prior squared error
0.4886 ± 0.0002
Root mean squared error
0.2236 ± 0.014
Root relative squared error
0.4577 ± 0.0288
SciMark benchmark
1910.4834

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