DEVELOPMENT... OpenML
Run
50877

Run 50877

Task 1748 (Learning Curve) tic-tac-toe Uploaded 06-11-2014 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)_I10
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.

21 Evaluation measures

Area under ROC curve
0.7994 ± 0.0435
Per class
Build CPU time
0.0057
Build memory
128356427.7333
F measure
0.7007 ± 0.0425
Per class
Kappa
0.3231 ± 0.095
KB relative information score
0.2275 ± 0.0512
Mean absolute error
0.3535 ± 0.0195
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
0.7065 ± 0.0444
Per class
Predictive accuracy
0.7182 ± 0.0391
Prior entropy
0.931 ± 0.0039
Recall
0.7182 ± 0.0391
Per class
Relative absolute error
0.7804 ± 0.0423
Root mean prior squared error
0.4759 ± 0.0014
Root mean squared error
0.412 ± 0.0193
Root relative squared error
0.8659 ± 0.04
SciMark benchmark
942.4911