DEVELOPMENT... OpenML
Run
161405

Run 161405

Task 6566 (Supervised Classification) Internet-Advertisements Uploaded 16-02-2015 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)_I160
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
0.969
Per class
F measure
0.9739
Per class
Kappa
0.8896
KB relative information score
2676.7832
Mean absolute error
0.0401
Mean prior absolute error
0.2409
Number of instances
3279
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.974
Per class
Predictive accuracy
0.9744
Prior entropy
0.5848
Recall
0.9744
Per class
Relative absolute error
0.1664
Root mean prior squared error
0.347
Root mean squared error
0.1504
Root relative squared error
0.4335
SciMark benchmark
943.2817
UserCPU time millis
183660
UserCPU time millis testing
10
UserCPU time millis training
183650