DEVELOPMENT... OpenML
Run
157399

Run 157399

Task 57 (Supervised Classification) 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
0.9578
Per class
F measure
0.9107
Per class
Kappa
0.8043
KB relative information score
265.6927
Mean absolute error
0.1152
Mean prior absolute error
0.4604
Number of instances
351
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9117
Per class
Predictive accuracy
0.9117
Prior entropy
0.9425
Recall
0.9117
Per class
Relative absolute error
0.2503
Root mean prior squared error
0.4797
Root mean squared error
0.2574
Root relative squared error
0.5365
SciMark benchmark
931.2336
UserCPU time millis
50
UserCPU time millis testing
10
UserCPU time millis training
40