title: difference between LinearRegression and svm.SVR(kernel="linear")
body: <p>First there are questions on this forum very similar to this one but trust me none matches so no duplicating please.</p>
  
  <p>I have encountered two methods of linear regression using scikit's sklearn and I am failing to understand the difference between the two, especially where in first code there's a method train_test_split() called while in the other one directly fit method is called. </p>
  
  <p>I am studying with multiple resources and this single issue is very confusing to me.</p>
  
  <p>First which uses SVR</p>
  
  <pre><code>X = np.array(df.drop(['label'], 1))
  
  X = preprocessing.scale(X)
  
  y = np.array(df['label'])
  
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
  
  clf = svm.SVR(kernel='linear')
  
  clf.fit(X_train, y_train)
  
  confidence = clf.score(X_test, y_test)
  </code></pre>
  
  <p>And second is this one</p>
  
  <pre><code># Split the data into training/testing sets
  diabetes_X_train = diabetes_X[:-20]
  diabetes_X_test = diabetes_X[-20:]
  
  # Split the targets into training/testing sets
  diabetes_y_train = diabetes.target[:-20]
  diabetes_y_test = diabetes.target[-20:]
  
  # Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(diabetes_X_train, diabetes_y_train)
  
  # Make predictions using the testing set
  diabetes_y_pred = regr.predict(diabetes_X_test)
  </code></pre>
  
  <p>So my main focus is the difference between using svr(kernel="linear") and using LinearRegression()</p>