DEVELOPMENT... OpenML
Run
160271

Run 160271

Task 3922 (Supervised Classification) desharnais Uploaded 13-02-2015 by Felicia West
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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)_I80
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.

22 Evaluation measures

Area under ROC curve
0.8286
Per class
F measure
0.7723
Per class
Kappa
0.5881
KB relative information score
47.0443
Mean absolute error
0.1589
Mean prior absolute error
0.3804
Number of instances
81
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7899
Per class
Predictive accuracy
0.7778
Prior entropy
1.3765
Recall
0.7778
Per class
Relative absolute error
0.4178
Root mean prior squared error
0.4348
Root mean squared error
0.3681
Root relative squared error
0.8467
SciMark benchmark
943.2817
UserCPU time millis
50
UserCPU time millis training
50