DEVELOPMENT... OpenML
Run
160161

Run 160161

Task 3813 (Supervised Classification) arsenic-female-lung 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)_I80
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
0.8936
Per class
F measure
0.9866
Per class
Kappa
0.7816
KB relative information score
305.4456
Mean absolute error
0.0159
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
0.9869
Per class
Predictive accuracy
0.9875
Prior entropy
0.222
Recall
0.9875
Per class
Relative absolute error
0.2371
Root mean prior squared error
0.1812
Root mean squared error
0.1121
Root relative squared error
0.6185
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40