DEVELOPMENT... OpenML
Run
157390

Run 157390

Task 48 (Supervised Classification) heart-c 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)_I20
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
0.8729
Per class
F measure
0.7842
Per class
Kappa
0.5642
KB relative information score
156.9182
Mean absolute error
0.0912
Mean prior absolute error
0.2004
Number of instances
303
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.786
Per class
Predictive accuracy
0.7855
Prior entropy
1.0792
Recall
0.7855
Per class
Relative absolute error
0.4551
Root mean prior squared error
0.315
Root mean squared error
0.2499
Root relative squared error
0.7934
SciMark benchmark
940.2922
UserCPU time millis
70
UserCPU time millis training
70