run_ml.md
In mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

run_ml works for random forest with grouping & feature importance

Code
  res <- mikropml::run_ml(otu_mini_bin, "rf", outcome_colname = "dx",
    find_feature_importance = TRUE, seed = 2019, cv_times = 2, groups = otu_mini_group)
Message
  Using 'dx' as the outcome column.
  Fraction of data in the training set: 0.8 
    Groups in the training set: A B C D E 
    Groups in the testing set: F
  Groups will be kept together in CV partitions
  Training the model...
  Training complete.
  Finding feature importance...
  Feature importance complete.