Run
59107

Run 59107

Task 2094 (Learning Curve) adult Uploaded 15-11-2014 by Jan van Rijn
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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
0.8854 ± 0.003
Per class
Build CPU time
0.0654
Build memory
3789613787.24
F measure
0.8336 ± 0.0035
Per class
Kappa
0.5233 ± 0.0104
KB relative information score
0.3538 ± 0.0061
Mean absolute error
0.226 ± 0.0017
Mean prior absolute error
0.3641 ± 0
Number of instances
48842
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.8344 ± 0.0037
Per class
Predictive accuracy
0.8429 ± 0.0032
Prior entropy
0.7938 ± 0.0002
Recall
0.8429 ± 0.0032
Per class
Relative absolute error
0.6209 ± 0.0048
Root mean prior squared error
0.4266 ± 0.0001
Root mean squared error
0.332 ± 0.0021
Root relative squared error
0.7781 ± 0.0049
SciMark benchmark
1769.7895