DEVELOPMENT... OpenML
Run
159441

Run 159441

Task 3836 (Supervised Classification) wine Uploaded 12-02-2015 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)_I40
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
0.9947
Per class
F measure
0.966
Per class
Kappa
0.9287
KB relative information score
166.2939
Mean absolute error
0.0306
Mean prior absolute error
0.4798
Number of instances
178
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9681
Per class
Predictive accuracy
0.9663
Prior entropy
0.971
Recall
0.9663
Per class
Relative absolute error
0.0638
Root mean prior squared error
0.4897
Root mean squared error
0.1467
Root relative squared error
0.2996
SciMark benchmark
945.6434
UserCPU time millis
20
UserCPU time millis training
20