DEVELOPMENT... OpenML
Run
157203

Run 157203

Task 3863 (Supervised Classification) hypothyroid Uploaded 11-02-2015 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)_I10
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
0.9983
Per class
F measure
0.9955
Per class
Kappa
0.9687
KB relative information score
3238.6258
Mean absolute error
0.0172
Mean prior absolute error
0.1426
Number of instances
3772
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9956
Per class
Predictive accuracy
0.9955
Prior entropy
0.3928
Recall
0.9955
Per class
Relative absolute error
0.1206
Root mean prior squared error
0.2668
Root mean squared error
0.0712
Root relative squared error
0.267
SciMark benchmark
929.566
UserCPU time millis
120
UserCPU time millis training
120