DEVELOPMENT... OpenML
Run
145041

Run 145041

Task 3756 (Supervised Classification) visualizing_hamster 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
0.2815
Per class
KB relative information score
-8.0401
Mean absolute error
0.5479
Mean prior absolute error
0.4955
Number of instances
73
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.2044
Per class
Predictive accuracy
0.4521
Prior entropy
0.9937
Recall
0.4521
Per class
Relative absolute error
1.1058
Root mean prior squared error
0.4977
Root mean squared error
0.7402
Root relative squared error
1.4873
SciMark benchmark
948.1045