DEVELOPMENT... OpenML
Run
158812

Run 158812

Task 50 (Supervised Classification) heart-h 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
0.8543
Per class
F measure
0.7872
Per class
Kappa
0.535
KB relative information score
137.5906
Mean absolute error
0.0914
Mean prior absolute error
0.1867
Number of instances
294
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7865
Per class
Predictive accuracy
0.7891
Prior entropy
1.0314
Recall
0.7891
Per class
Relative absolute error
0.4893
Root mean prior squared error
0.3037
Root mean squared error
0.2534
Root relative squared error
0.8344
SciMark benchmark
932.3943
UserCPU time millis
90
UserCPU time millis training
90