DEVELOPMENT... OpenML
Run
145021

Run 145021

Task 3738 (Supervised Classification) rabe_131 Uploaded 31-01-2015 by Felicia West
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Flow

weka.AdaBoostM1_BayesianLogisticRegression(2)Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
weka.AdaBoostM1_BayesianLogisticRegression(2)_I10
weka.AdaBoostM1_BayesianLogisticRegression(2)_P100
weka.AdaBoostM1_BayesianLogisticRegression(2)_S1
weka.AdaBoostM1_BayesianLogisticRegression(2)_Wweka.classifiers.bayes.BayesianLogisticRegression
weka.BayesianLogisticRegression(2)_Dtrue
weka.BayesianLogisticRegression(2)_F2
weka.BayesianLogisticRegression(2)_H1
weka.BayesianLogisticRegression(2)_I100
weka.BayesianLogisticRegression(2)_Ntrue
weka.BayesianLogisticRegression(2)_P1
weka.BayesianLogisticRegression(2)_RR:0.01-316,3.16
weka.BayesianLogisticRegression(2)_S0.5
weka.BayesianLogisticRegression(2)_Tl5.0E-4
weka.BayesianLogisticRegression(2)_V0.27
weka.BayesianLogisticRegression(2)_seed1

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
0.5
Per class
F measure
0.2485
Per class
KB relative information score
-10.1012
Mean absolute error
0.58
Mean prior absolute error
0.4877
Number of instances
50
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.1764
Per class
Predictive accuracy
0.42
Prior entropy
0.9829
Recall
0.42
Per class
Relative absolute error
1.1893
Root mean prior squared error
0.4936
Root mean squared error
0.7616
Root relative squared error
1.543
SciMark benchmark
930.32