DEVELOPMENT... OpenML
Run
157340

Run 157340

Task 6566 (Supervised Classification) Internet-Advertisements 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
0.9411
Per class
F measure
0.9529
Per class
Kappa
0.7936
KB relative information score
2190.6635
Mean absolute error
0.0812
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
0.9561
Per class
Predictive accuracy
0.9558
Prior entropy
0.5848
Recall
0.9558
Per class
Relative absolute error
0.3369
Root mean prior squared error
0.347
Root mean squared error
0.1897
Root relative squared error
0.5467
SciMark benchmark
929.566
UserCPU time millis
12510
UserCPU time millis testing
20
UserCPU time millis training
12490