DEVELOPMENT... OpenML
Run
159648

Run 159648

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)_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
0.6696
Per class
F measure
0.5479
Per class
Kappa
0.1935
KB relative information score
17.7944
Mean absolute error
0.219
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
0.5442
Per class
Predictive accuracy
0.5541
Prior entropy
1.5899
Recall
0.5541
Per class
Relative absolute error
0.7543
Root mean prior squared error
0.3782
Root mean squared error
0.4534
Root relative squared error
1.1989
SciMark benchmark
924.6116
UserCPU time millis
240
UserCPU time millis training
240