text: """Plan for a random forest classifier model."""
  from dbispipeline.evaluators import GridEvaluator
  import dbispipeline.result_handlers as result_handlers
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  
  from nlp4musa2020.dataloaders.alf200k import ALF200KLoader
  from nlp4musa2020.dataloaders.alf200k import genre_target_labels
  import nlp4musa2020.evaluators as evaluators
  
  dataloader = ALF200KLoader(
      path='data/processed/dataset-lfm-genres.pickle',
      load_feature_groups=[
          'statistical',
      ],
      text_vectorizers=None,
      target=genre_target_labels(),
  )
  
  pipeline = Pipeline([
      ('scaler', StandardScaler()),
      ('model', RandomForestClassifier(n_jobs=-1)),
  ])
  
  evaluator = GridEvaluator(
      parameters={
          'model__n_estimators': [10, 100, 300],
      },
      grid_parameters=evaluators.grid_parameters_genres(),
  )
  
  result_handlers = [
      result_handlers.print_gridsearch_results,
  ]
  