I have researched the internet|literature a lot on multiclass prediction to find out what is a realistic limit for the number of classes that can successfully be used for estimation when using a RandomForest method.
The literature body on text mining sometimes comes up with really large numbers of classes (>1000), while most other "classical" cases described have a class count less than 6-8. Most of them describe handmade algorithms specifically designed for the particular problem, though, while I am interested in the performance of standard RF implementations (in R, for example).
I have even started to analyse simulated data to find out more about it, but the problem is to generate data that simulates a lot of multiple classes yet has meaningful and realistic predictors.
I know that the results depend largely on the number of observations in every class and the balance between class outcomes. For my data, I can safely assume that there will be enough observations per class, so that I can balance the data accordingly.
So I am curious whether people have applied standard RandomForest implementations to multiclass problems with a class count >>10. Note that I am not talking about separating the estimation into multiple one-vs-all problems.
Does anybody here have some real-life experience with that kind of data?