text: from dffml import Features, Feature
  from dffml.noasync import train, score, 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,
      {"Years": 0, "Expertise": 1, "Trust": 0.1, "Salary": 10},
      {"Years": 1, "Expertise": 3, "Trust": 0.2, "Salary": 20},
      {"Years": 2, "Expertise": 5, "Trust": 0.3, "Salary": 30},
      {"Years": 3, "Expertise": 7, "Trust": 0.4, "Salary": 40},
  )
  
  # Assess accuracy
  scorer = MeanSquaredErrorAccuracy()
  print(
      "Accuracy:",
      score(
          model,
          scorer,
          Feature("Salary", int, 1),
          {"Years": 4, "Expertise": 9, "Trust": 0.5, "Salary": 50},
          {"Years": 5, "Expertise": 11, "Trust": 0.6, "Salary": 60},
      ),
  )
  
  # 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)
  