DEVELOPMENT... OpenML
Run
50275

Run 50275

Task 1722 (Learning Curve) cmc Uploaded 06-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.AdaBoostM1_REPTree(2)Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
weka.AdaBoostM1_REPTree(2)_I10
weka.AdaBoostM1_REPTree(2)_P100
weka.AdaBoostM1_REPTree(2)_S1
weka.AdaBoostM1_REPTree(2)_Wweka.classifiers.trees.REPTree
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

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
0.6815 ± 0.0304
Per class
Build CPU time
0.0175
Build memory
2894006775.76
F measure
0.5118 ± 0.0294
Per class
Kappa
0.2417 ± 0.0439
KB relative information score
0.1561 ± 0.0351
Mean absolute error
0.3938 ± 0.0145
Mean prior absolute error
0.4308 ± 0.0004
Number of instances
1473
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.5118 ± 0.0317
Per class
Predictive accuracy
0.5126 ± 0.0291
Prior entropy
1.539 ± 0.0029
Recall
0.5126 ± 0.0291
Per class
Relative absolute error
0.9141 ± 0.0342
Root mean prior squared error
0.4641 ± 0.0005
Root mean squared error
0.4431 ± 0.0079
Root relative squared error
0.9547 ± 0.0175
SciMark benchmark
1655.4655