DEVELOPMENT... OpenML
Run
161129

Run 161129

Task 3813 (Supervised Classification) arsenic-female-lung Uploaded 15-02-2015 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
0.9193
Per class
F measure
0.9849
Per class
Kappa
0.7574
KB relative information score
317.1288
Mean absolute error
0.0157
Mean prior absolute error
0.0672
Number of instances
559
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9848
Per class
Predictive accuracy
0.9857
Prior entropy
0.222
Recall
0.9857
Per class
Relative absolute error
0.2339
Root mean prior squared error
0.1812
Root mean squared error
0.1165
Root relative squared error
0.6429
SciMark benchmark
936.6206
UserCPU time millis
70
UserCPU time millis training
70