DEVELOPMENT... OpenML
Run
159496

Run 159496

Task 3899 (Supervised Classification) mozilla4 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
0.9626
Per class
F measure
0.9388
Per class
Kappa
0.8589
KB relative information score
12281.7409
Mean absolute error
0.0995
Mean prior absolute error
0.4412
Number of instances
15545
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9438
Per class
Predictive accuracy
0.9403
Prior entropy
0.9135
Recall
0.9403
Per class
Relative absolute error
0.2255
Root mean prior squared error
0.4697
Root mean squared error
0.2211
Root relative squared error
0.4706
SciMark benchmark
911.0478
UserCPU time millis
950
UserCPU time millis testing
10
UserCPU time millis training
940