impute_model: Generate a model to impute missing data in a column
In jeroenvdhoven/datapiper: datapiper

We don't recommend to impute categorical variables with many levels.

1 2	impute_model(data, column, NA_value = is.na, exclude_columns, controls = NA, type = "xgboost")

`data`	The dataset, as a data.frame.
`column`	The column to be imputed. Should be a string.
`NA_value`	A function to define what a NA is for this column which returns TRUE when a value is missing and FALSE otherwise. Will take one column vector as input.
`exclude_columns`	Columns that shouldn't be included in the imputation.
`controls`	Either: NA for defaults A list for params from xgboost. Should always contain at least `nrounds`.
`type`	The type of algorithm to use for imputation. Options: mean, lm, and xgboost. Mean will calculate the mean for numeric sets and the mode for non-numerics.