DEVELOPMENT... OpenML
Run
156789

Run 156789

Task 2257 (Supervised Classification) BNG(cylinder-bands) Uploaded 11-02-2015 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)_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.

22 Evaluation measures

Area under ROC curve
0.7806
Per class
F measure
0.7238
Per class
Kappa
0.4303
KB relative information score
249925.9671
Mean absolute error
0.3785
Mean prior absolute error
0.4878
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7295
Per class
Predictive accuracy
0.7299
Prior entropy
0.9823
Recall
0.7299
Per class
Relative absolute error
0.776
Root mean prior squared error
0.4939
Root mean squared error
0.4275
Root relative squared error
0.8656
SciMark benchmark
940.2922
UserCPU time millis
223260
UserCPU time millis testing
1130
UserCPU time millis training
222130