imputation FOR random forests 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!
 A: You shouldn't as what imputation method is best for your model, but rather how will the imputation method affect your model.  
I think the best approach is to compare imputation methods in your cross validation.  Thus, the imputation essentially becomes a sort of hyperparameter.
Anyway, the answer is highly context dependent, and can really only be answered via cross validation.  What works for one model/problem/dataset may not work for the next.
A: I would say, it strongly depends 'where' the data are missing.
Only in your training set? or in the test set too? do you expect missing data when actually predicting on new data?
If you have missing data in your test set as well, and you do some sophisticated single-imputations based on a regression or something like that, then you work with the 'most-likely' values in both sets. This will lower your testing-error as you artificially take variance out of the data 
A: okay, so omitting more data, will probably not be possible 
In my opinion, there is one common mistake with imputations: people tend to use those imputation that lead to a better model fit. But of course the model is not getting better, they imputed just the fitting values.
A way to generally lower that risk are multiple imputations...but it's not possible to combine this method with randomforests (yet), at least as far as I know. 
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.
I don't think that is correct. I would describe randomforests as 'very forgiving' to imputing a 'mean'.
Even if that 'mean' would be terribly wrong, the 'random' part of an 'randomForest' deals with that easily
The reason I feel so apprehensive abourt using class mean imputation is that it reduces the internal variance of each class, and I'm worried that that is artificially making each class more homogeneous internally, and more different from the other classes. That would then make the classification results artificially better than they would be if I was using a method that didn't distort the distributions. Is that wrong?
It's completely the other way around--let's say you have two observations. X1 is 100kg and height unknown. X2 is 50 kilo and height unknown. You are using a regression-prediction as the imputation. The most likely height for X1 is 1.90m and for X2 1.50m. So your Residuals have a very low variance and might not display the reality, where people do not always have the most likely height for their weight.
also , if those two observations get splittet up in training and test, it will be more easy for them to "find the patterns" as their values were created in the same data-generating process.
instead if you are using the mean, your error's variance will usuallly get higher
A: Your point "the distribution of each predictor is important" is always the case. Your conclusion from this fact "so using a simple method like class-mean imputation would be incorrect, because it distorts the distribution of the predictors" is not so clear to me. Why can't you use class mean imputation as a first strategy? 
