DEVELOPMENT... OpenML
Run
157338

Run 157338

Task 7287 (Supervised Classification) adult 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
0.8852
Per class
Average cost
0.0786
F measure
0.8335
Per class
Kappa
0.5231
KB relative information score
17270.9945
Mean absolute error
0.2261
Mean prior absolute error
0.3641
Number of instances
48842
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8343
Per class
Predictive accuracy
0.8427
Prior entropy
0.7939
Recall
0.8427
Per class
Relative absolute error
0.6209
Root mean prior squared error
0.4266
Root mean squared error
0.332
Root relative squared error
0.7781
SciMark benchmark
940.2922
Total cost
3840.5
UserCPU time millis
1140
UserCPU time millis testing
50
UserCPU time millis training
1090