Besides imputation approaches offered by some random forests algorithms, all tree based methods provide another intuitive strategy for missing value handling: This strategy is that, at first, observations that have missing values in the variable that is currently evaluated are ignored in the computation of the impurity reduction for this variable. However, the same observations are included in all other computations, so that the method does not involve cancelation of observations with missing values (which can result in heavy data loss).
