DEVELOPMENT... OpenML
Run
158998

Run 158998

Task 206 (Supervised Classification) BNG(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
0.76
Per class
F measure
0.717
Per class
Kappa
0.3619
KB relative information score
8058.7967
Mean absolute error
0.3653
Mean prior absolute error
0.4532
Number of instances
39366
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7191
Per class
Predictive accuracy
0.729
Prior entropy
0.9315
Recall
0.729
Per class
Relative absolute error
0.806
Root mean prior squared error
0.476
Root mean squared error
0.4276
Root relative squared error
0.8982
SciMark benchmark
938.2848
UserCPU time millis
950
UserCPU time millis testing
30
UserCPU time millis training
920