DEVELOPMENT... OpenML
Run
159547

Run 159547

Task 3946 (Supervised Classification) KDDCup09_churn 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)_I40
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
0.7301
Per class
F measure
0.8926
Per class
Kappa
0.0172
KB relative information score
-18107.582
Mean absolute error
0.1277
Mean prior absolute error
0.1361
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
0.8899
Per class
Predictive accuracy
0.9263
Prior entropy
0.3787
Recall
0.9263
Per class
Relative absolute error
0.938
Root mean prior squared error
0.2609
Root mean squared error
0.2534
Root relative squared error
0.9715
SciMark benchmark
933.1776
UserCPU time millis
168390
UserCPU time millis testing
80
UserCPU time millis training
168310