Run
55196

Run 55196

Task 2102 (Learning Curve) eucalyptus Uploaded 10-11-2014 by Jan van Rijn
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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
0.8965 ± 0.0161
Per class
Build CPU time
0.0343
Build memory
2003800363.5
F measure
0.6588 ± 0.0409
Per class
Kappa
0.5627 ± 0.0511
KB relative information score
0.5362 ± 0.0321
Mean absolute error
0.1785 ± 0.0105
Mean prior absolute error
0.3132 ± 0.0002
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.6622 ± 0.0409
Per class
Predictive accuracy
0.6603 ± 0.0403
Prior entropy
2.2621 ± 0.0043
Recall
0.6603 ± 0.0403
Per class
Relative absolute error
0.5697 ± 0.0334
Root mean prior squared error
0.3957 ± 0.0003
Root mean squared error
0.3037 ± 0.0138
Root relative squared error
0.7674 ± 0.0347
SciMark benchmark
1634.4504