User 3030 | 2/1/2016, 4:24:21 PM
I know rpart trees or gbm in R uses surrogate splitting, and xgboost library got it's own complex imputation strategy. What strategy does graphlab's implementation of boosted trees use to handle missing values? Also, if BoostedTreesClassifier tries to unpack a column of type dict (with numeric values), what value will be assigned to a row/observation corresponding to the unpacked column where a key-value pair is missing? Will it be assigned to zero or None?