DEVELOPMENT... OpenML
Run
55192

Run 55192

Task 2097 (Learning Curve) satimage Uploaded 10-11-2014 by Jason
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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
0.9782 ± 0.0035
Per class
Build CPU time
0.4934
Build memory
1947347499.7143
F measure
0.8531 ± 0.0101
Per class
Kappa
0.8201 ± 0.0125
KB relative information score
0.7984 ± 0.0106
Mean absolute error
0.0706 ± 0.0029
Mean prior absolute error
0.2701 ± 0
Number of instances
6430
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.853 ± 0.0094
Per class
Predictive accuracy
0.8546 ± 0.0101
Prior entropy
2.4834 ± 0.0009
Recall
0.8546 ± 0.0101
Per class
Relative absolute error
0.2612 ± 0.0108
Root mean prior squared error
0.3675 ± 0
Root mean squared error
0.1865 ± 0.0068
Root relative squared error
0.5074 ± 0.0186
SciMark benchmark
1606.2579