DEVELOPMENT... OpenML
Run
160290

Run 160290

Task 3948 (Supervised Classification) KDDCup09_upselling Uploaded 13-02-2015 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
0.8637
Per class
F measure
0.9438
Per class
Kappa
0.5425
KB relative information score
6220.946
Mean absolute error
0.0892
Mean prior absolute error
0.1364
Number of instances
50000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9455
Per class
Predictive accuracy
0.9506
Prior entropy
0.3794
Recall
0.9506
Per class
Relative absolute error
0.6541
Root mean prior squared error
0.2612
Root mean squared error
0.2101
Root relative squared error
0.8046
SciMark benchmark
924.6116
UserCPU time millis
319760
UserCPU time millis testing
70
UserCPU time millis training
319690