title: Pipeline: Multiple classifiers?
body: <p>I read following example on Pipelines and GridSearchCV in Python:
  <a href="http://www.davidsbatista.net/blog/2017/04/01/document_classification/" rel="noreferrer">http://www.davidsbatista.net/blog/2017/04/01/document_classification/</a></p>
  
  <p><strong>Logistic Regression:</strong></p>
  
  <pre><code>pipeline = Pipeline([
      ('tfidf', TfidfVectorizer(stop_words=stop_words)),
      ('clf', OneVsRestClassifier(LogisticRegression(solver='sag')),
  ])
  parameters = {
      'tfidf__max_df': (0.25, 0.5, 0.75),
      'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3)],
      "clf__estimator__C": [0.01, 0.1, 1],
      "clf__estimator__class_weight": ['balanced', None],
  }
  </code></pre>
  
  <p><strong>SVM:</strong></p>
  
  <pre><code>pipeline = Pipeline([
      ('tfidf', TfidfVectorizer(stop_words=stop_words)),
      ('clf', OneVsRestClassifier(LinearSVC()),
  ])
  parameters = {
      'tfidf__max_df': (0.25, 0.5, 0.75),
      'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3)],
      "clf__estimator__C": [0.01, 0.1, 1],
      "clf__estimator__class_weight": ['balanced', None],
  }
  </code></pre>
  
  <p>Is there a way that Logistic Regression and SVM could be combined into <em>one</em> Pipeline? Say, I have a TfidfVectorizer and like to test against multiple classifiers that each then output the best model/parameters.</p>