title: Is it possible to tune the linear regression (hyper)parameter in sklearn
body: <p>I'm starting to learn a bit of sci-kit learn and ML in general and i'm running into a problem.
  I've created a model using linear regression.
  the <code>.score</code> is good (above 0.8) but i want to get it better (perhaps to 0.9). 
  I've searched the documentation of sklearn and googled this question but I cannot seem to find the answer.</p>
  
  <p>My question is: <strong>Is it possible to tune the</strong> <code>LinearRegression</code> <strong>model?</strong> 
  and if so, where can I find it?</p>
  
  <pre><code>#----- Forecast in hours -----#
  forecast_out = 48
  
  
  #----- Import and prep data -----#
  using pandas to create X and y
  x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
  
  #----- Linear Regression-----#
  lr = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
  
  lr.fit(x_train, y_train)
  lr_confidence = lr.score(x_test, y_test)
  print("lr confidence: ", lr_confidence)
  x_forecast = np.array(data.drop(['Prediction'],1))[-forecast_out:]
  lr_prediction = lr.predict(x_forecast)
  </code></pre>