Run
55209

Run 55209

Task 1711 (Learning Curve) mfeat-factors Uploaded 10-11-2014 by Jan van Rijn
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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
0.9965 ± 0.0015
Per class
Build CPU time
2.7053
Build memory
1249621703.4182
F measure
0.9422 ± 0.0109
Per class
Kappa
0.9356 ± 0.0118
KB relative information score
0.935 ± 0.0114
Mean absolute error
0.0172 ± 0.0026
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9426 ± 0.0111
Per class
Predictive accuracy
0.942 ± 0.0106
Prior entropy
3.3219
Recall
0.942 ± 0.0106
Per class
Relative absolute error
0.0955 ± 0.0143
Root mean prior squared error
0.3
Root mean squared error
0.0919 ± 0.0095
Root relative squared error
0.3063 ± 0.0316
SciMark benchmark
1634.4504