  In this paper we present the practical benefits of a new random forest
algorithm to deal withmissing values in the sample. The purpose of this work is
to compare the different solutionsto deal with missing values with random
forests and describe our new algorithm performanceas well as its algorithmic
complexity. A variety of missing value mechanisms (such as MCAR,MAR, MNAR) are
considered and simulated. We study the quadratic errors and the bias ofour
algorithm and compare it to the most popular missing values random forests
algorithms inthe literature. In particular, we compare those techniques for
both a regression and predictionpurpose. This work follows a first paper
Gomez-Mendez and Joly (2020) on the consistency ofthis new algorithm.
