I haven't seen that done before, which may be most of the reason - I am not sure that is common practice.
I can see upsides - you need not pick candidate splits to test since there is only one N-way rule possible. The binary split equivalent is N levels instead of 1. The big problem is that you are always segregating the data set entirely, and making the equivalent of those N binary rules, even when you would not otherwise bother because they don't add information about the target. The subsets matching each child are therefore unnecessarily small and this makes learning on each independent subset weaker. On Nov 6, 2014 9:36 AM, "jamborta" <jambo...@gmail.com> wrote: > I meant above, that in the case of categorical variables it might be more > efficient to create a node on each categorical value. Is there a reason why > spark went down the binary route? > > thanks, > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/why-decision-trees-do-binary-split-tp18188p18265.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >
