DEVELOPMENT... OpenML
Run
159048

Run 159048

Task 2274 (Supervised Classification) meta_ensembles.arff Uploaded 12-02-2015 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)_I40
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
0.6856
Per class
F measure
0.568
Per class
Kappa
0.2255
KB relative information score
21.1328
Mean absolute error
0.2094
Mean prior absolute error
0.2904
Number of instances
74
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5605
Per class
Predictive accuracy
0.5811
Prior entropy
1.5899
Recall
0.5811
Per class
Relative absolute error
0.7212
Root mean prior squared error
0.3782
Root mean squared error
0.431
Root relative squared error
1.1398
SciMark benchmark
924.6116
UserCPU time millis
90
UserCPU time millis training
90