Thanks for the reply, Sean. I can see that splitting on all the categories would probably overfit the tree, on the other hand, it might give more insight on the subcategories (probably only would work if the data is uniformly distributed between the categories).
I haven't really found any comparison between the two methods in terms of performance and interpretability. thanks, On Thu, Nov 6, 2014 at 9:46 AM, Sean Owen <so...@cloudera.com> wrote: > 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 >> > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
