If don't predefine imputer, "let the optimizer find the optimal imputer" in "Classification AutoML with imputation", the optimal imputer will encounter a problem that it "does not accept missing values encoded as NaN natively". Shouldn't it first judge whether there is null data in the dataset? Otherwise what's the meaning of "optimal"?
If don't predefine imputer, "let the optimizer find the optimal imputer" in "Classification AutoML with imputation", the optimal imputer will encounter a problem that it "does not accept missing values encoded as NaN natively". Shouldn't it first judge whether there is null data in the dataset? Otherwise what's the meaning of "optimal"?