mlim: Multiple Imputation with Automated Machine Learning
Using automated machine learning, the package fine-tunes an Elastic
Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
or Stacked Ensemble machine learning model for imputing the missing
observations of each variable. This procedure has been implemented for the
first time by this package and is expected to outperform other packages for
imputing missing data that do not fine-tune their models. The main idea is
to allow the model to set its own parameters for imputing each variable
instead of setting fixed predefined parameters to impute all variables
of the dataset.
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