DEVELOPMENT... OpenML
Run
53533

Run 53533

Task 1702 (Learning Curve) kr-vs-kp Uploaded 08-11-2014 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
0.9764 ± 0.0064
Per class
Build CPU time
0.0078
Build memory
989403085.2
F measure
0.9382 ± 0.0092
Per class
Kappa
0.8762 ± 0.0183
KB relative information score
0.7053 ± 0.0191
Mean absolute error
0.1678 ± 0.0096
Mean prior absolute error
0.499 ± 0.0001
Number of instances
3196
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.9394 ± 0.0086
Per class
Predictive accuracy
0.9384 ± 0.0091
Prior entropy
0.9986 ± 0.0002
Recall
0.9384 ± 0.0091
Per class
Relative absolute error
0.3363 ± 0.0193
Root mean prior squared error
0.4995 ± 0.0001
Root mean squared error
0.2423 ± 0.0125
Root relative squared error
0.4851 ± 0.025
SciMark benchmark
1914.93