DEVELOPMENT... OpenML
Run
157374

Run 157374

Task 30 (Supervised Classification) page-blocks 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
0.9904
Per class
F measure
0.9693
Per class
Kappa
0.8388
KB relative information score
4227.9181
Mean absolute error
0.017
Mean prior absolute error
0.0762
Number of instances
5473
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9689
Per class
Predictive accuracy
0.9702
Prior entropy
0.6391
Recall
0.9702
Per class
Relative absolute error
0.2237
Root mean prior squared error
0.1949
Root mean squared error
0.096
Root relative squared error
0.4926
SciMark benchmark
933.8635
UserCPU time millis
1740
UserCPU time millis testing
10
UserCPU time millis training
1730

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