text: """Config for a linear regression model evaluated on a diabetes dataset."""
  from dbispipeline.evaluators import GridEvaluator
  import dbispipeline.result_handlers as result_handlers
  from sklearn.svm import LinearSVR
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  
  from nlp4musa2020.dataloaders.vectorizer import lda, tfidf
  from nlp4musa2020.dataloaders.alf200k import ALF200KLoader, genre_target_labels
  import nlp4musa2020.evaluators as evaluators
  from sklearn.svm import LinearSVC
  from sklearn.multioutput import MultiOutputClassifier
  
  dataloader = ALF200KLoader(
      path='data/processed/dataset-lfm-genres.pickle',
      load_feature_groups=[
          'rhymes',
          'statistical',
          'statistical_time',
          'explicitness',
      ],
      text_vectorizers=lda() + tfidf(),
      target=genre_target_labels()
  )
  
  pipeline = Pipeline([
      ('scaler', StandardScaler()),
      ('model', MultiOutputClassifier(LinearSVC())),
  ])
  
  evaluator = GridEvaluator(
      parameters={
          'model__estimator__C': [
              0.1,
              0.5,
              1.0,
              2.0,
              5.0,
          ],
          'model__estimator__loss': ['epsilon_insensitive'],
      },
      grid_parameters=evaluators.grid_parameters_genres(),
  )
  
  result_handlers = [
      result_handlers.print_gridsearch_results,
  ]
  