DEVELOPMENT... OpenML
Run
55204

Run 55204

Task 1713 (Learning Curve) mfeat-fourier 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
0.971 ± 0.0051
Per class
Build CPU time
0.7594
Build memory
1076112052.5818
F measure
0.7797 ± 0.0355
Per class
Kappa
0.755 ± 0.0409
KB relative information score
0.7768 ± 0.0232
Mean absolute error
0.0586 ± 0.0043
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.7805 ± 0.0329
Per class
Predictive accuracy
0.7795 ± 0.0369
Prior entropy
3.3219
Recall
0.7795 ± 0.0369
Per class
Relative absolute error
0.3253 ± 0.024
Root mean prior squared error
0.3
Root mean squared error
0.1737 ± 0.0094
Root relative squared error
0.5789 ± 0.0315
SciMark benchmark
1606.2579