DEVELOPMENT... OpenML
Run
55217

Run 55217

Task 1740 (Learning Curve) soybean 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
0.9958 ± 0.0024
Per class
Build CPU time
0.0807
Build memory
2021131308.1
F measure
0.9379 ± 0.0262
Per class
Kappa
0.9326 ± 0.0282
KB relative information score
0.9439 ± 0.015
Mean absolute error
0.0096 ± 0.0018
Mean prior absolute error
0.0961 ± 0.0001
Number of instances
683
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9391 ± 0.0234
Per class
Predictive accuracy
0.9385 ± 0.0258
Prior entropy
3.8358 ± 0.0227
Recall
0.9385 ± 0.0258
Per class
Relative absolute error
0.0994 ± 0.0185
Root mean prior squared error
0.2191 ± 0.0002
Root mean squared error
0.0669 ± 0.0103
Root relative squared error
0.3055 ± 0.047
SciMark benchmark
1634.4504