DEVELOPMENT... OpenML
Run
50288

Run 50288

Task 1748 (Learning Curve) tic-tac-toe 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
0.9871 ± 0.0097
Per class
Build CPU time
0.0127
Build memory
1347331068.9778
F measure
0.9538 ± 0.0226
Per class
Kappa
0.8973 ± 0.0508
KB relative information score
0.8792 ± 0.0501
Mean absolute error
0.055 ± 0.0221
Mean prior absolute error
0.453 ± 0.0013
Number of instances
958
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9542 ± 0.0207
Per class
Predictive accuracy
0.9541 ± 0.0221
Prior entropy
0.931 ± 0.0039
Recall
0.9541 ± 0.0221
Per class
Relative absolute error
0.1215 ± 0.0487
Root mean prior squared error
0.4759 ± 0.0014
Root mean squared error
0.1933 ± 0.0471
Root relative squared error
0.4063 ± 0.099
SciMark benchmark
1655.4655