Editing Decision tree learning (section)

===Decision graphs===
In a decision tree, all paths from the root node to the leaf node proceed by way of conjunction, or ''AND''. In a decision graph, it is possible to use disjunctions (ORs) to join two more paths together using [[minimum message length]] (MML).<ref>{{cite web | url=http://citeseer.ist.psu.edu/oliver93decision.html | title=CiteSeerX}}</ref>  Decision graphs have been further extended to allow for previously unstated new attributes to be learnt dynamically and used at different places within the graph.<ref>[http://www.csse.monash.edu.au/~dld/Publications/2003/Tan+Dowe2003_MMLDecisionGraphs.pdf Tan & Dowe (2003)]</ref>  The more general coding scheme results in better predictive accuracy and log-loss probabilistic scoring.{{Citation needed|date=January 2012}}  In general, decision graphs infer models with fewer leaves than decision trees.