DEVELOPMENT... OpenML
Run
158811

Run 158811

Task 49 (Supervised Classification) tic-tac-toe Uploaded 12-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I40
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

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.

22 Evaluation measures

Area under ROC curve
0.9192
Per class
F measure
0.8165
Per class
Kappa
0.5855
KB relative information score
359.6668
Mean absolute error
0.2996
Mean prior absolute error
0.453
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
0.8301
Per class
Predictive accuracy
0.8257
Prior entropy
0.9312
Recall
0.8257
Per class
Relative absolute error
0.6614
Root mean prior squared error
0.4759
Root mean squared error
0.3524
Root relative squared error
0.7406
SciMark benchmark
910.8389
UserCPU time millis
30
UserCPU time millis training
30

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