DEVELOPMENT... OpenML
Run
158810

Run 158810

Task 48 (Supervised Classification) heart-c 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
0.8718
Per class
F measure
0.8015
Per class
Kappa
0.5994
KB relative information score
160.0551
Mean absolute error
0.0891
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
0.8018
Per class
Predictive accuracy
0.802
Prior entropy
1.0792
Recall
0.802
Per class
Relative absolute error
0.4445
Root mean prior squared error
0.315
Root mean squared error
0.2472
Root relative squared error
0.7848
SciMark benchmark
932.3943
UserCPU time millis
90
UserCPU time millis training
90