DEVELOPMENT... OpenML
Run
160756

Run 160756

Task 2274 (Supervised Classification) meta_ensembles.arff Uploaded 14-02-2015 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)_I160
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
0.6319
Per class
F measure
0.5408
Per class
Kappa
0.1915
KB relative information score
16.9175
Mean absolute error
0.2238
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
0.5438
Per class
Predictive accuracy
0.5405
Prior entropy
1.5899
Recall
0.5405
Per class
Relative absolute error
0.7706
Root mean prior squared error
0.3782
Root mean squared error
0.4627
Root relative squared error
1.2235
SciMark benchmark
932.3943
UserCPU time millis
470
UserCPU time millis training
470

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