DEVELOPMENT... OpenML
Run
52174

Run 52174

Task 1748 (Learning Curve) tic-tac-toe Uploaded 07-11-2014 by Felicia West
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Flow

weka.RealAdaBoost_IBk(1)J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
weka.IBk(2)_Atrue
weka.IBk(2)_K1
weka.IBk(2)_W0
weka.RealAdaBoost_IBk(1)_P100
weka.RealAdaBoost_IBk(1)_H1.0
weka.RealAdaBoost_IBk(1)_S1
weka.RealAdaBoost_IBk(1)_I10
weka.RealAdaBoost_IBk(1)_Wweka.classifiers.lazy.IBk

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
0.9994 ± 0.0006
Per class
Build CPU time
0.1885
Build memory
1025330213.1556
F measure
0.9895 ± 0.0111
Per class
Kappa
0.9768 ± 0.0248
KB relative information score
0.9695 ± 0.021
Mean absolute error
0.0137 ± 0.009
Mean prior absolute error
0.453 ± 0.0013
Number of instances
958
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9897 ± 0.0106
Per class
Predictive accuracy
0.9896 ± 0.011
Prior entropy
0.931 ± 0.0039
Recall
0.9896 ± 0.011
Per class
Relative absolute error
0.0303 ± 0.0199
Root mean prior squared error
0.4759 ± 0.0014
Root mean squared error
0.0976 ± 0.0445
Root relative squared error
0.2051 ± 0.0937
SciMark benchmark
946.7958