DEVELOPMENT... OpenML
Run
160356

Run 160356

Task 49 (Supervised Classification) tic-tac-toe Uploaded 13-02-2015 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)_I160
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
0.9921
Per class
F measure
0.9821
Per class
Kappa
0.9603
KB relative information score
682.4175
Mean absolute error
0.1586
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
0.9827
Per class
Predictive accuracy
0.9823
Prior entropy
0.9312
Recall
0.9823
Per class
Relative absolute error
0.3501
Root mean prior squared error
0.4759
Root mean squared error
0.2016
Root relative squared error
0.4237
SciMark benchmark
924.6116
UserCPU time millis
70
UserCPU time millis training
70