DEVELOPMENT... OpenML
Run
157462

Run 157462

Task 3947 (Supervised Classification) KDDCup99 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
1
Per class
F measure
0.9993
Per class
Kappa
0.9989
KB relative information score
492352.6606
Mean absolute error
0.0002
Mean prior absolute error
0.0514
Number of instances
494020
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9993
Per class
Predictive accuracy
0.9993
Prior entropy
1.5581
Recall
0.9993
Per class
Relative absolute error
0.0033
Root mean prior squared error
0.1603
Root mean squared error
0.0074
Root relative squared error
0.0459
SciMark benchmark
943.2817
UserCPU time millis
1687200
UserCPU time millis testing
1860
UserCPU time millis training
1685340