DEVELOPMENT... OpenML
Run
50882

Run 50882

Task 1752 (Learning Curve) vehicle 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
0.9108 ± 0.0195
Per class
Build CPU time
0.0231
Build memory
280198808.7111
F measure
0.703 ± 0.0426
Per class
Kappa
0.622 ± 0.0496
KB relative information score
0.567 ± 0.0401
Mean absolute error
0.2 ± 0.0149
Mean prior absolute error
0.3748 ± 0
Number of instances
846
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.699 ± 0.0432
Per class
Predictive accuracy
0.7163 ± 0.0372
Prior entropy
1.9991 ± 0.0005
Recall
0.7163 ± 0.0372
Per class
Relative absolute error
0.5336 ± 0.0397
Root mean prior squared error
0.4329 ± 0
Root mean squared error
0.3017 ± 0.0193
Root relative squared error
0.6969 ± 0.0446
SciMark benchmark
1651.8608