DEVELOPMENT... OpenML
Run
55214

Run 55214

Task 1736 (Learning Curve) diabetes 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
0.8187 ± 0.0417
Per class
Build CPU time
0.0055
Build memory
1710558050.3
F measure
0.758 ± 0.0301
Per class
Kappa
0.4583 ± 0.0667
KB relative information score
0.3297 ± 0.0383
Mean absolute error
0.3082 ± 0.0159
Mean prior absolute error
0.4545 ± 0.0015
Number of instances
768
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.7585 ± 0.0296
Per class
Predictive accuracy
0.7643 ± 0.0285
Prior entropy
0.9331 ± 0.0046
Recall
0.7643 ± 0.0285
Per class
Relative absolute error
0.678 ± 0.0344
Root mean prior squared error
0.4766 ± 0.0016
Root mean squared error
0.4059 ± 0.0232
Root relative squared error
0.8515 ± 0.0481
SciMark benchmark
1634.4504