By the way, in this years’ post—or, better yet, see the dynamic document here, in our predictive models we use elastic-net and random-forest modeling approaches with validation (cross-fold validation for tuning on training data, predictive power and model performance measured on set-aside testing data).
For missing data, we do a combination of simple imputations (for continuous variables) and ‘coding non-responses as separate categories’ (for categorical data).
By the way, in this years’ post—or, better yet, see the dynamic document here, in our predictive models we use elastic-net and random-forest modeling approaches with validation (cross-fold validation for tuning on training data, predictive power and model performance measured on set-aside testing data).
For missing data, we do a combination of simple imputations (for continuous variables) and ‘coding non-responses as separate categories’ (for categorical data).