DEVELOPMENT... OpenML
Run
53551

Run 53551

Task 1735 (Learning Curve) segment 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
0.9975 ± 0.0012
Per class
Build CPU time
0.1064
Build memory
1072355521.8667
F measure
0.9585 ± 0.0124
Per class
Kappa
0.9515 ± 0.0145
KB relative information score
0.9413 ± 0.0105
Mean absolute error
0.0222 ± 0.0031
Mean prior absolute error
0.2449
Number of instances
2310
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.9589 ± 0.0122
Per class
Predictive accuracy
0.9584 ± 0.0124
Prior entropy
2.8074
Recall
0.9584 ± 0.0124
Per class
Relative absolute error
0.0906 ± 0.0126
Root mean prior squared error
0.3499
Root mean squared error
0.0947 ± 0.0116
Root relative squared error
0.2707 ± 0.0332
SciMark benchmark
1914.93

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