I'm having trouble getting decision forests to work with categorical features. I have a dataset with a categorical feature with 40 values. It seems to be treated as a continuous/numeric value by the implementation.
Digging deeper, I see there is some logic in the code that indicates that categorical features over N values do not work unless the number of bins is at least 2*((2^N - 1) - 1) bins. I understand this as the naive brute force condition, wherein the decision tree will test all possible splits of the categorical value. However, this gets unusable quickly as the number of bins should be tens or hundreds at best, and this requirement rules out categorical values over more than 10 or so features as a result. But, of course, it's not unusual to have categorical features with high cardinality. It's almost common. There are some pretty fine heuristics for selecting 'bins' over categorical features when the number of bins is far fewer than the complete, exhaustive set. Before I open a JIRA or continue, does anyone know what I am talking about, am I mistaken? Is this a real limitation and is it worth pursuing these heuristics? I can't figure out how to proceed with decision forests in MLlib otherwise. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
