Below, you find output typical for a 10 fold cross-validation run in the Weka Explorer with comments where the output is specific for Bayesian nets.

=== Run information === Scheme: weka.classifiers.bayes.BayesNet -D -B iris.xml -Q weka.classifiers.bayes.net.search.local.K2 -- -P 2 -S BAYES -E weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5Options for

Relation: iris-weka.filters.unsupervised.attribute.Discretize-B2-M-1.0-Rfirst-last Instances: 150 Attributes: 5 sepallength sepalwidth petallength petalwidth class Test mode: 10-fold cross-validation === Classifier model (full training set) === Bayes Network Classifier not using ADTreeIndication whether the ADTree algorithm [10] for calculating counts in the data set was used.

#attributes=5 #classindex=4This line lists the number of attribute and the number of the class variable for which the classifier was trained.

Network structure (nodes followed by parents) sepallength(2): class sepalwidth(2): class petallength(2): class sepallength petalwidth(2): class petallength class(3):This list specifies the network structure. Each of the variables is followed by a list of parents, so the variable has parents and , while has no parents. The number in braces is the cardinality of the variable. It shows that in the iris dataset there are three class variables. All other variables are made binary by running it through a discretization filter.

LogScore Bayes: -374.9942769685747 LogScore BDeu: -351.85811477631626 LogScore MDL: -416.86897021246466 LogScore ENTROPY: -366.76261727150217 LogScore AIC: -386.76261727150217These lines list the logarithmic score of the network structure for various methods of scoring.

If a BIF file was specified, the following two lines will be produced (if no such file was specified, no information is printed).

Missing: 0 Extra: 2 Reversed: 0 Divergence: -0.0719759699700729

In this case the network that was learned was compared with a file `iris.xml`
which contained the naive Bayes network structure. The number after ``Missing''
is the number of arcs that was in the network in file that is not recovered by
the structure learner. Note that a reversed arc is not counted as missing.
The number after ``Extra'' is the number of arcs in the learned network that are
not in the network on file. The number of reversed arcs is listed as well.

Finally, the divergence between the network distribution on file and the one learned is reported. This number is calculated by enumerating all possible instantiations of all variables, so it may take some time to calculate the divergence for large networks.

The remainder of the output is standard output for all classifiers.

Time taken to build model: 0.01 seconds === Stratified cross-validation === === Summary === Correctly Classified Instances 116 77.3333 % Incorrectly Classified Instances 34 22.6667 %etc...