  Incomplete data are common in practical applications. Most predictive machine
learning models do not handle missing values so they require some
preprocessing. Although many algorithms are used for data imputation, we do not
understand the impact of the different methods on the predictive models'
performance. This paper is first that systematically evaluates the empirical
effectiveness of data imputation algorithms for predictive models. The main
contributions are (1) the recommendation of a general method for empirical
benchmarking based on real-life classification tasks and the (2) comparative
analysis of different imputation methods for a collection of data sets and a
collection of ML algorithms.
