Michael Mayer handles missing data:

{missRanger} is a multivariate imputation algorithm based on random forests, and a fast version of the original missForest algorithm of Stekhoven and Buehlmann (2012). Surprise, surprise: it uses {ranger} to fit random forests. Especially combined with predictive mean matching (PMM), the imputations are often quite realistic.

This looks like an interesting package. At first, I thought it was a way of generating predictions outside the boundaries of training data and had concerns—a classic point (limitation?) of random forest as an algorithm is that it will not even try to predict values outside the range of what it sees in training data, so if the largest label is 10 and the smallest is 0, you won’t see a prediction of 11 or 50, no matter how you scale the inputs.

Instead of doing that, missRanger looks like it’s filling in missing data using a clever approach. That’s quite useful for dealing with incomplete data, a really common problem whose good solutions tend to be complex enough that people typically ignore them in favor of simple but less useful solutions like dropping rows altogether.

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