DEVELOPMENT... OpenML
Run
160343

Run 160343

Task 34 (Supervised Classification) postoperative-patient-data Uploaded 13-02-2015 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)_I160
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
0.3706
Per class
F measure
0.6064
Per class
Kappa
0.0121
KB relative information score
-8.5834
Mean absolute error
0.3035
Mean prior absolute error
0.2871
Number of instances
90
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5944
Per class
Predictive accuracy
0.6778
Prior entropy
1.0305
Recall
0.6778
Per class
Relative absolute error
1.0572
Root mean prior squared error
0.3755
Root mean squared error
0.4366
Root relative squared error
1.1627
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis training
30