DEVELOPMENT... OpenML
Run
51798

Run 51798

Task 1710 (Learning Curve) balance-scale Uploaded 07-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.MultiBoostAB_REPTree(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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.MultiBoostAB_REPTree(2)_C3
weka.MultiBoostAB_REPTree(2)_P100
weka.MultiBoostAB_REPTree(2)_S1
weka.MultiBoostAB_REPTree(2)_I10
weka.MultiBoostAB_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
0.9573 ± 0.0158
Per class
Build CPU time
0.0066
Build memory
320085463.6
F measure
0.8091 ± 0.0409
Per class
Kappa
0.7049 ± 0.053
KB relative information score
0.6439 ± 0.039
Mean absolute error
0.1105 ± 0.0169
Mean prior absolute error
0.3798 ± 0.0012
Number of instances
625
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.7804 ± 0.0437
Per class
Predictive accuracy
0.84 ± 0.0302
Prior entropy
1.3181 ± 0.0124
Recall
0.84 ± 0.0302
Per class
Relative absolute error
0.2911 ± 0.0444
Root mean prior squared error
0.4356 ± 0.0014
Root mean squared error
0.2994 ± 0.0241
Root relative squared error
0.6874 ± 0.0551
SciMark benchmark
1638.2006