DEVELOPMENT... OpenML
Run
158769

Run 158769

Task 5 (Supervised Classification) arrhythmia 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
0.8632
Per class
F measure
0.6948
Per class
Kappa
0.559
KB relative information score
250.1108
Mean absolute error
0.0352
Mean prior absolute error
0.0855
Number of instances
452
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6768
Per class
Predictive accuracy
0.7212
Prior entropy
2.5222
Recall
0.7212
Per class
Relative absolute error
0.4115
Root mean prior squared error
0.2055
Root mean squared error
0.1693
Root relative squared error
0.8239
SciMark benchmark
902.4773
UserCPU time millis
10400
UserCPU time millis training
10400