DEVELOPMENT... OpenML
Run
160021

Run 160021

Task 3647 (Supervised Classification) rabe_266 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
0.9936
Per class
F measure
0.9667
Per class
Kappa
0.9334
KB relative information score
110.5349
Mean absolute error
0.0404
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
0.0809
Root mean prior squared error
0.4994
Root mean squared error
0.1725
Root relative squared error
0.3455
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10