DEVELOPMENT... OpenML
Run
51805

Run 51805

Task 1711 (Learning Curve) mfeat-factors 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
0.9956 ± 0.0019
Per class
Build CPU time
1.0244
Build memory
225379607.7091
F measure
0.9471 ± 0.0185
Per class
Kappa
0.9411 ± 0.0203
KB relative information score
0.9469 ± 0.0169
Mean absolute error
0.0113 ± 0.0036
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9476 ± 0.0165
Per class
Predictive accuracy
0.947 ± 0.0183
Prior entropy
3.3219
Recall
0.947 ± 0.0183
Per class
Relative absolute error
0.0626 ± 0.0202
Root mean prior squared error
0.3
Root mean squared error
0.0951 ± 0.0161
Root relative squared error
0.317 ± 0.0538
SciMark benchmark
1638.2006