DEVELOPMENT... OpenML
Run
158809

Run 158809

Task 43 (Supervised Classification) spambase 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
0.9812
Per class
F measure
0.9414
Per class
Kappa
0.877
KB relative information score
3777.1554
Mean absolute error
0.0916
Mean prior absolute error
0.4776
Number of instances
4601
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9415
Per class
Predictive accuracy
0.9415
Prior entropy
0.9674
Recall
0.9415
Per class
Relative absolute error
0.1918
Root mean prior squared error
0.4886
Root mean squared error
0.2133
Root relative squared error
0.4366
SciMark benchmark
910.8389
UserCPU time millis
2030
UserCPU time millis training
2030