DEVELOPMENT... OpenML
Run
160240

Run 160240

Task 3532 (Supervised Classification) tr12.wc 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
0.9764
Per class
F measure
0.8925
Per class
Kappa
0.8728
KB relative information score
273.9171
Mean absolute error
0.0273
Mean prior absolute error
0.2079
Number of instances
313
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8918
Per class
Predictive accuracy
0.8946
Prior entropy
2.7783
Recall
0.8946
Per class
Relative absolute error
0.1312
Root mean prior squared error
0.3222
Root mean squared error
0.1554
Root relative squared error
0.4823
SciMark benchmark
943.2817
UserCPU time millis
1015660
UserCPU time millis training
1015660