DEVELOPMENT... OpenML
Run
55207

Run 55207

Task 1720 (Learning Curve) car 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
0.9708 ± 0.0112
Per class
Build CPU time
0.0079
Build memory
219991031.4182
F measure
0.8609 ± 0.0316
Per class
Kappa
0.7065 ± 0.068
KB relative information score
0.5872 ± 0.0326
Mean absolute error
0.1132 ± 0.0075
Mean prior absolute error
0.229 ± 0.0007
Number of instances
1728
Per class
OS information
[Oracle Corporation, 1.7.0_65, amd64, Linux, 3.5.0-54-generic]
Precision
0.8714 ± 0.0309
Per class
Predictive accuracy
0.8646 ± 0.0304
Prior entropy
1.2058 ± 0.0093
Recall
0.8646 ± 0.0304
Per class
Relative absolute error
0.4942 ± 0.0331
Root mean prior squared error
0.3381 ± 0.001
Root mean squared error
0.2208 ± 0.011
Root relative squared error
0.6531 ± 0.0329
SciMark benchmark
1910.4834

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