>RandomForest and other decision tree methods can actually handle missing data very well by treating an individual cell in the data matrix as missing This is w

>RandomForest and other decision tree methods can actually handle missing data very well by treating an individual cell in the data matrix as missing This is why I tend to favor these over regression or other methods. Real data is going to have lots of missing values, sometimes too many for imputation to even work. Whittling down your data to only those which have all predictor fields filled in can drastically shrink down your data set.