DEVELOPMENT... OpenML
Run
160231

Run 160231

Task 3884 (Supervised Classification) page-blocks 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
0.9861
Per class
F measure
0.969
Per class
Kappa
0.8299
KB relative information score
3894.9069
Mean absolute error
0.0437
Mean prior absolute error
0.1838
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
0.9688
Per class
Predictive accuracy
0.9693
Prior entropy
0.4768
Recall
0.9693
Per class
Relative absolute error
0.2377
Root mean prior squared error
0.3031
Root mean squared error
0.1543
Root relative squared error
0.5091
SciMark benchmark
924.6116
UserCPU time millis
1110
UserCPU time millis testing
10
UserCPU time millis training
1100