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Can XGBoost handle missing value?
XGBoost supports missing values by default. In tree algorithms, branch directions for missing values are learned during training. Note that the gblinear booster treats missing values as zeros.
What is the best imputation method for replacing missing values in a numerical feature?
A popular approach for data imputation is to calculate a statistical value for each column (such as a mean) and replace all missing values for that column with the statistic. It is a popular approach because the statistic is easy to calculate using the training dataset and because it often results in good performance.
How does XGBoost handle missing values for split candidate?
Specifically, they create a default direction for those nodes with, as splitting feature, one with missing values in the current instance set. At prediction time, if the prediction path goes through this node and the feature value is missing, the default direction is followed.
How to beat the XGBoost built-in strategy?
In order to beat the XGBoost built-in strategy we have to consider at the same time all the features of a sample and somehow deal with the possible presence of more than a missing value in it. A good example of such an approach is a K-Nearest Neighbours (KNN) with an ad-hoc distance metric to properly deal with missing values.
Is there a default branch for F in XGBoost?
So even if f contains some missings in the training dataset, this node doesn’t have a default branch. A test instance falling here, would be stuck. Maybe you are right and the default branch will be the one with more examples, if no missings are present.
Is the not imputing missing values the right choice?
And not imputing missing values isn’t always the right choice. Nowadays is quite easy to have decent results in data science tasks: it’s sufficient to have a gener a l understanding of the process, a basic knowledge of Python and ten minutes of your time to instantiate XGBoost and fit the model.