DEVELOPMENT... OpenML
Run
158215

Run 158215

Task 3852 (Supervised Classification) anneal 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)_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
0.9893
Per class
F measure
0.9878
Per class
Kappa
0.9665
KB relative information score
810.6213
Mean absolute error
0.0411
Mean prior absolute error
0.3633
Number of instances
898
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9879
Per class
Predictive accuracy
0.9878
Prior entropy
0.7932
Recall
0.9878
Per class
Relative absolute error
0.1132
Root mean prior squared error
0.426
Root mean squared error
0.1127
Root relative squared error
0.2644
SciMark benchmark
932.3943
UserCPU time millis
50
UserCPU time millis training
50