next up previous contents
Next: Bayesian networks in GUI Up: Inspecting Bayesian networks Previous: Inspecting Bayesian networks   Contents

Bayesian networks in text

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 0.5
Options for BayesNet include the class names for the structure learner and for the distribution estimator.
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 ADTree
Indication whether the ADTree algorithm [10] for calculating counts in the data set was used.
#attributes=5 #classindex=4
This 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 $petallength$ variable has parents $sepallength$ and $class$, while $class$ 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: -386.76261727150217
These 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...


next up previous contents
Next: Bayesian networks in GUI Up: Inspecting Bayesian networks Previous: Inspecting Bayesian networks   Contents
Remco Bouckaert 2008-05-12