DEVELOPMENT... OpenML
Run
159611

Run 159611

Task 39 (Supervised Classification) sonar Uploaded 12-02-2015 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
0.9005
Per class
F measure
0.826
Per class
Kappa
0.65
KB relative information score
130.881
Mean absolute error
0.1872
Mean prior absolute error
0.4978
Number of instances
208
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8289
Per class
Predictive accuracy
0.8269
Prior entropy
0.9968
Recall
0.8269
Per class
Relative absolute error
0.3762
Root mean prior squared error
0.4989
Root mean squared error
0.3763
Root relative squared error
0.7542
SciMark benchmark
938.2848
UserCPU time millis
190
UserCPU time millis training
190