DEVELOPMENT... OpenML
Run
157448

Run 157448

Task 215 (Supervised Classification) BNG(ionosphere) 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)_I20
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
0.9664
Per class
F measure
0.9143
Per class
Kappa
0.8129
KB relative information score
730435.0789
Mean absolute error
0.1318
Mean prior absolute error
0.4602
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9143
Per class
Predictive accuracy
0.9147
Prior entropy
0.9418
Recall
0.9147
Per class
Relative absolute error
0.2865
Root mean prior squared error
0.4797
Root mean squared error
0.2536
Root relative squared error
0.5286
SciMark benchmark
902.4773
UserCPU time millis
51750
UserCPU time millis testing
910
UserCPU time millis training
50840