DEVELOPMENT... OpenML
Run
160160

Run 160160

Task 3812 (Supervised Classification) arsenic-female-bladder 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
0.8038
Per class
F measure
0.8316
Per class
Kappa
0.2796
KB relative information score
-1.3165
Mean absolute error
0.1886
Mean prior absolute error
0.2462
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
0.825
Per class
Predictive accuracy
0.8408
Prior entropy
0.5956
Recall
0.8408
Per class
Relative absolute error
0.7663
Root mean prior squared error
0.3502
Root mean squared error
0.3398
Root relative squared error
0.9704
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40