where each feature is imputed using other features as predictor variables for the regression model. Sequential Regression Multivariate Imputation (SRMI) improves upon this by fitting a sequence of regression models and drawing values from the corresponding predictive distributions [@raghunathan2001multivariate]. Incremental Attribute Regression Imputation (IARI) constructs a sequence of regression models to iteratively impute the missing values and also uses the class label of each sample as a predictor variable [@van2016incremental]. In Multiple Imputation using Chained Equations (MICE), the conditional distribution of each missing feature must be specified given the other features [@buuren1999flexible]. It is assumed that the feature matrix has a full multivariate distribution from which the conditional distribution of each feature is derived. The full distribution need not be specified, as long as the distribution of each feature is stated, a feature called fully conditional specification [@van2007multiple]. MICE can handle mixed types of data. It has options for predictive mean matching, linear regression, binary and polytomous logistic regression, etc., and uses the Gibbs sampler to generate multiple imputations. However, for a given set of conditional distributions, a multivariate distribution may not exist [@van2006fully]. The ideas of MICE and SRMI are combined in the MissForest approach [@stekhoven2011missforest] which fits a 