DEVELOPMENT... OpenML
Run
156617

Run 156617

Task 39 (Supervised Classification) sonar 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
0.8151
Per class
F measure
0.7259
Per class
Kappa
0.4491
KB relative information score
82.9745
Mean absolute error
0.3062
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
0.7258
Per class
Predictive accuracy
0.726
Prior entropy
0.9968
Recall
0.726
Per class
Relative absolute error
0.6153
Root mean prior squared error
0.4989
Root mean squared error
0.4317
Root relative squared error
0.8654
SciMark benchmark
938.9865
UserCPU time millis
20
UserCPU time millis training
20