In MSDN article
Microsoft Decision Trees Algorithm Technical Reference is said "The Microsoft Decision Trees algorithm learns Bayesian networks..". It seems to me that BN and DT are very different tasks and approaches to data modelling. The question is: how learned
Bayesian network (which models joint distribution considering some conditional independencies) is used further to build decision tree?
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