DEVELOPMENT... OpenML
Run
157419

Run 157419

Task 137 (Supervised Classification) BNG(autos,nominal,1000000) 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)_I20
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
0.9254
Per class
F measure
0.7114
Per class
Kappa
0.6319
KB relative information score
579646.0411
Mean absolute error
0.1186
Mean prior absolute error
0.221
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7151
Per class
Predictive accuracy
0.7197
Prior entropy
2.3035
Recall
0.7197
Per class
Relative absolute error
0.5366
Root mean prior squared error
0.3324
Root mean squared error
0.2356
Root relative squared error
0.7089
SciMark benchmark
902.4773
UserCPU time millis
237670
UserCPU time millis testing
1480
UserCPU time millis training
236190