DEVELOPMENT... OpenML
Run
50880

Run 50880

Task 1742 (Learning Curve) spambase Uploaded 06-11-2014 by Jason
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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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
0.9685 ± 0.0095
Per class
Build CPU time
0.0554
Build memory
473495532.9143
F measure
0.9209 ± 0.0121
Per class
Kappa
0.8341 ± 0.0255
KB relative information score
0.7424 ± 0.0229
Mean absolute error
0.1321 ± 0.0106
Mean prior absolute error
0.4776 ± 0.0002
Number of instances
4601
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.9209 ± 0.0119
Per class
Predictive accuracy
0.9211 ± 0.012
Prior entropy
0.9674 ± 0.0006
Recall
0.9211 ± 0.012
Per class
Relative absolute error
0.2766 ± 0.0223
Root mean prior squared error
0.4886 ± 0.0002
Root mean squared error
0.2515 ± 0.0162
Root relative squared error
0.5147 ± 0.0334
SciMark benchmark
1948.3582