DEVELOPMENT... OpenML
Run
156734

Run 156734

Task 219 (Supervised Classification) electricity Uploaded 11-02-2015 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)_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.

22 Evaluation measures

Area under ROC curve
0.8541
Per class
F measure
0.7653
Per class
Kappa
0.5175
KB relative information score
17566.6859
Mean absolute error
0.3142
Mean prior absolute error
0.4886
Number of instances
45312
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7778
Per class
Predictive accuracy
0.772
Prior entropy
0.9835
Recall
0.772
Per class
Relative absolute error
0.6431
Root mean prior squared error
0.4943
Root mean squared error
0.3907
Root relative squared error
0.7905
SciMark benchmark
941.7954
UserCPU time millis
1260
UserCPU time millis testing
30
UserCPU time millis training
1230