I was wondering what imputation method you would recommend for data to be fed into a random forest model for a classification problem.
If you google for "imputation for random forests", you get a lot of results about imputation with/by random forests, but next to nothing for imputation for random forests.
My understanding is that for random forest models, the distribution of each predictor is important, so using a simple method like class-mean imputation would be incorrect, because it distorts the distribution of the predictors.
So, what imputation method(s) would you recommend for random forests?
In my specific case, I'm building this model for 2 purposes, but please feel free to answer the question in a more general way. My 2 purposes are:
- predicting the class of future, unlabelled samples with no missing values
- determine what the most important features are, and build a story based on that
And if I need to give more detail, please let me know. (I feel if I include a lot of detail to begin with it'll be distracting)
Thanks very much!