DEVELOPMENT... OpenML
Run
160873

Run 160873

Task 3561 (Supervised Classification) profb 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
0.7368
Per class
F measure
0.7107
Per class
Kappa
0.3425
KB relative information score
148.8345
Mean absolute error
0.3424
Mean prior absolute error
0.4446
Number of instances
672
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7085
Per class
Predictive accuracy
0.7143
Prior entropy
0.9188
Recall
0.7143
Per class
Relative absolute error
0.7701
Root mean prior squared error
0.4714
Root mean squared error
0.4404
Root relative squared error
0.9342
SciMark benchmark
945.6434
UserCPU time millis
140
UserCPU time millis training
140