DEVELOPMENT... OpenML
Run
160048

Run 160048

Task 3679 (Supervised Classification) chscase_vine2 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
0.8968
Per class
F measure
0.8269
Per class
Kappa
0.6506
KB relative information score
246.4089
Mean absolute error
0.2465
Mean prior absolute error
0.4956
Number of instances
468
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8269
Per class
Predictive accuracy
0.8269
Prior entropy
0.9937
Recall
0.8269
Per class
Relative absolute error
0.4975
Root mean prior squared error
0.4978
Root mean squared error
0.3583
Root relative squared error
0.7197
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40