RandomForest factor with too many levels I have a model with about 200,000 training observations, where I am regressing, with 4 factors and 2 continuous variables.  One of my features has 927 levels, which is causing the R implementation of randomForest to fail (it has a limit of 32 levels for any feature).  Unfortunately, I don't see a simple way to avoid using this factor, or to decompose it into a series of continuous variables.  Since my predictors are a mix of categorical and continuous, I thought of trees.  Can anyone suggest a different implementation (package or language), ML approach, or a better way to pre-process or massage my inputs?  
 A: I'm not an R user, but some general comments about random forest
Discrete features can be treated two ways depending on their property


*

*if they have a clear ordering, eg year, (bad/med/good) result, then you can just treat them as continuous.  

*if they can't, eg gender,company name, then they should be dummy'd.

A: You might try representing that one column differently. You could represent the same data as a sparse dataframe with dummy variables. 
Minimum viable code; 
example <- as.data.frame(c("A", "A", "B", "F", "C", "G", "C", "D", "E", "F"))
names(example) <- "strcol"

for(level in unique(example$strcol)){
      example[paste("dummy", level, sep = "_")] <- ifelse(example$strcol == level,     1, 0)
}

A: Have you tried cforest in party package? I know it can handle more than 30-40 levels but I am not sure about 900 levels.
http://www.inside-r.org/packages/cran/party/docs/cforest
A: You can use sklearn (python) and use OneHotEncoding. 
OneHotEncoder basically creates one column per each level for a categorical variable (927 in your case). There created columns can take only 0 or 1 as values. This method by definition puts no limit on the number of levels. However, it does not capture the interaction between levels the same way as R does. The reason cforest doesn't allow you to have more than 30 levels is the huge number of permutations (~2^30).
Sklearn aside, I suggest finding a way to reduce your number of levels.  
