DEVELOPMENT... OpenML
Run
160210

Run 160210

Task 3862 (Supervised Classification) audiology 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
0.9842
Per class
F measure
0.9598
Per class
Kappa
0.8926
KB relative information score
195.8424
Mean absolute error
0.0454
Mean prior absolute error
0.3783
Number of instances
226
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9599
Per class
Predictive accuracy
0.9602
Prior entropy
0.8182
Recall
0.9602
Per class
Relative absolute error
0.12
Root mean prior squared error
0.4343
Root mean squared error
0.198
Root relative squared error
0.456
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40