DEVELOPMENT... OpenML
Run
160962

Run 160962

Task 3647 (Supervised Classification) rabe_266 Uploaded 15-02-2015 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
0.9933
Per class
F measure
0.9667
Per class
Kappa
0.9334
KB relative information score
110.6874
Mean absolute error
0.0394
Mean prior absolute error
0.4988
Number of instances
120
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
0.9667
Prior entropy
0.9983
Recall
0.9667
Per class
Relative absolute error
0.0789
Root mean prior squared error
0.4994
Root mean squared error
0.1762
Root relative squared error
0.3529
SciMark benchmark
936.6206
UserCPU time millis
10
UserCPU time millis training
10