text: from dffml import CSVSource, Features, Feature
  from dffml.noasync import train, accuracy, predict
  from dffml_model_scikit import LinearRegressionModel
  from dffml.accuracy import MeanSquaredErrorAccuracy
  
  model = LinearRegressionModel(
      features=Features(
          Feature("Years", int, 1),
          Feature("Expertise", int, 1),
          Feature("Trust", float, 1),
      ),
      predict=Feature("Salary", int, 1),
      location="tempdir",
  )
  
  # Train the model
  train(model, "training.csv")
  
  # Assess accuracy (alternate way of specifying data source)
  scorer = MeanSquaredErrorAccuracy()
  print("Accuracy:", accuracy(model, scorer, CSVSource(filename="test.csv")))
  
  # Make prediction
  for i, features, prediction in predict(
      model,
      {"Years": 6, "Expertise": 13, "Trust": 0.7},
      {"Years": 7, "Expertise": 15, "Trust": 0.8},
  ):
      features["Salary"] = prediction["Salary"]["value"]
      print(features)
  