  Missing data is an expected issue when large amounts of data is collected,
and several imputation techniques have been proposed to tackle this problem.
Beneath classical approaches such as MICE, the application of Machine Learning
techniques is tempting. Here, the recently proposed missForest imputation
method has shown high imputation accuracy under the Missing (Completely) at
Random scheme with various missing rates. In its core, it is based on a random
forest for classification and regression, respectively. In this paper we study
whether this approach can even be enhanced by other methods such as the
stochastic gradient tree boosting method, the C5.0 algorithm or modified random
forest procedures. In particular, other resampling strategies within the random
forest protocol are suggested. In an extensive simulation study, we analyze
their performances for continuous, categorical as well as mixed-type data.
Therein, MissBooPF, a combination of the stochastic gradient tree boosting
method together with the parametrically bootstrapped random forest method,
appeared to be promising. Finally, an empirical analysis focusing on credit
information and Facebook data is conducted.
