title: Linear regression gives worse results after normalization or standardization
body: <p>I'm performing linear regression on this dataset:
  archive.ics.uci.edu/ml/datasets/online+news+popularity</p>
  
  <p>It contains various types of features - rates, binary, numbers etc.</p>
  
  <p>I've tried using scikit-learn Normalizer, StandardScaler and PowerTransformer, but the've all resulted in worse results than without using them.</p>
  
  <p>I'm using them like this:</p>
  
  <pre><code>from sklearn.preprocessing import StandardScaler
  X = df.drop(columns=['url', 'shares'])
  Y = df['shares']
  transformer = StandardScaler().fit(X)
  X_scaled = transformer.transform(X)
  X_scaled = pd.DataFrame(X_scaled, columns=X.columns)
  perform_linear_and_ridge_regression(X=X_scaled, Y=Y)
  </code></pre>
  
  <p>The function on the last line <code>perform_linear_and_ridge_regression()</code> is correct for sure and is using GridSearchCV to determine the best hyperparameters.</p>
  
  <p>Just to make sure I include the function as well:</p>
  
  <pre><code>def perform_linear_and_ridge_regression(X, Y):
      X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=10) 
      lin_reg_parameters = { 'fit_intercept': [True, False] }
      lin_reg = GridSearchCV(LinearRegression(), lin_reg_parameters, cv=5)
      lin_reg.fit(X=X_train, y=Y_train)
      Y_pred = lin_reg.predict(X_test)
      print('Linear regression MAE =', median_absolute_error(Y_test, Y_pred))
  </code></pre>
  
  <p>The results are surprising as all of them provide worse results:</p>
  
  <p>Linear reg. on original data: MAE = 1620.510555135375</p>
  
  <p>Linear reg. after using Normalizer: MAE = 1979.8525218964242</p>
  
  <p>Linear reg. after using StandardScaler: MAE = 2915.024521207241</p>
  
  <p>Linear reg. after using PowerScaler: MAE = 1663.7148884463259</p>
  
  <p>Is this just a special case, where Standardization doesn't help, or am I doing something wrong?</p>
  
  <p>EDIT: Even when I leave the binary features out, most of the transformers gives worse results.</p>