DEVELOPMENT... OpenML
Run
160319

Run 160319

Task 4 (Supervised Classification) labor 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)_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
0.9378
Per class
F measure
0.9298
Per class
Kappa
0.8459
KB relative information score
46.8965
Mean absolute error
0.0771
Mean prior absolute error
0.457
Number of instances
57
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9298
Per class
Predictive accuracy
0.9298
Prior entropy
0.9393
Recall
0.9298
Per class
Relative absolute error
0.1686
Root mean prior squared error
0.4773
Root mean squared error
0.269
Root relative squared error
0.5636
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20