R's randomForest package can not handle factor with more than 32 levels. When it is given more than 32 levels, it emits an error message:
Can not handle categorical predictors with more than 32 categories.
But the data I have has several factors. Some of them have 1000+ levels and some of them have 100+. It even has 'state' of united states which is 52.
So, here's my question.
Why is there such limitation? randomForest refuse to run even for the simple case.
> d <- data.frame(x=factor(1:50), y=1:50) > randomForest(y ~ x, data=d) Error in randomForest.default(m, y, ...) : Can not handle categorical predictors with more than 32 categories.
If it is simply due to memory limitation, how can scikit learn's randomForeestRegressor run with more than 32 levels?
What is the best way to handle this problem? Suppose that I have X1, X2, ..., X50 independent variables and Y is dependent variable. And suppose that X1, X2 and X3 has more than 32 levels. What should I do?
What I'm thinking of is running clustering algorithm for each of X1, X2 and X3 where distance is defined as difference in Y. I'll run three clusterings as there are three problematic variables. And in each clustering, I wish I can find similar levels. And I'll merge them.
How does this sound?
randomForestcan handle categorical predictors with up to 53 levels. News $\endgroup$