DEVELOPMENT... OpenML
Run
55211

Run 55211

Task 1730 (Learning Curve) credit-g Uploaded 10-11-2014 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
0.7319 ± 0.037
Per class
Build CPU time
0.0049
Build memory
146076184.2667
F measure
0.7066 ± 0.0497
Per class
Kappa
0.2767 ± 0.123
KB relative information score
0.1437 ± 0.0446
Mean absolute error
0.3514 ± 0.0132
Mean prior absolute error
0.4202
Number of instances
1000
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.7041 ± 0.0569
Per class
Predictive accuracy
0.724 ± 0.0493
Prior entropy
0.8813
Recall
0.724 ± 0.0493
Per class
Relative absolute error
0.8365 ± 0.0313
Root mean prior squared error
0.4583
Root mean squared error
0.4293 ± 0.0167
Root relative squared error
0.9369 ± 0.0363
SciMark benchmark
1910.4834