i wanted to build a prediction model. Since my data had some missing data, I imputed data with the MICE algorithm. After that I wanted to do a regression with Random Forest.
Now I'm kinda stuck because:
I wanted to do Multiple Imputation with MICE because I wanted to show consideration for the variance of the missing variables in my model. So I imputed 5 data sets with MICE.
If i wanted to do a glm, I would build 5 models(for each imputed data set) and then pool them together. (Meaning in the end i have 1 model and the variance of my parameters will be higher)
Now what I want to do is to build a random forest. But I just can't find any strategies for this. Since RF doesn't have parameter estimations, I can't pool them together...
Has anyone worked on this before? or any advices what I should do?
Best wishes and thank you in advance! I really appreciate any help and answers