DEVELOPMENT... OpenML
Run
156991

Run 156991

Task 3653 (Supervised Classification) triazines Uploaded 11-02-2015 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)_I10
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
0.8082
Per class
F measure
0.7596
Per class
Kappa
0.501
KB relative information score
63.7352
Mean absolute error
0.3319
Mean prior absolute error
0.4854
Number of instances
186
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7625
Per class
Predictive accuracy
0.7634
Prior entropy
0.979
Recall
0.7634
Per class
Relative absolute error
0.6839
Root mean prior squared error
0.4925
Root mean squared error
0.4153
Root relative squared error
0.8432
SciMark benchmark
931.2336
UserCPU time millis
20
UserCPU time millis training
20