title: How to classify text with text and boolean features?
body: <div class="s-table-container">
  <table class="s-table">
  <thead>
  <tr>
  <th>Label</th>
  <th>TextFeature</th>
  <th>BoolFeature1</th>
  <th>BoolFeature2</th>
  <th>BoolFeature3</th>
  </tr>
  </thead>
  <tbody>
  <tr>
  <td>Label1</td>
  <td>TextFeature1</td>
  <td>1</td>
  <td>0</td>
  <td>0</td>
  </tr>
  <tr>
  <td>Label2</td>
  <td>TextFeature2</td>
  <td>0</td>
  <td>1</td>
  <td>0</td>
  </tr>
  <tr>
  <td>Label3</td>
  <td>TextFeature3</td>
  <td>0</td>
  <td>0</td>
  <td>1</td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>I am using SGD classification model for label prediction using text feature. Splitting the dataset goes pretty easy: text feature goes to X, label goes to y.</p>
  <pre><code>sgd_ppl_clf = Pipeline([
      ('tfidf', TfidfVectorizer()),
      ('sgd_clf', SGDClassifier(loss='modified_huber', penalty='l2',alpha=1e-3, random_state=42, max_iter=5, tol=None))])
  
  X = data.TextFeature
  y = data.Label
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)
  
  calibratedSgd = CalibratedClassifierCV(sgd_ppl_clf, method='sigmoid', cv=5)
  calibratedSgd.fit(X_train, y_train)
  
  predicted_sgd = calibratedSgd.predict(X_test)
  print(metrics.accuracy_score(y_test,predicted_sgd))
  </code></pre>
  <p>I want to try extra features that has a bool type to increase overall accuracy.
  Is there is a simple way to join text and bool features while splitting dataset?</p>