title: How to fix the number of label for XGBClassifer?
body: <p>I try to build a distinct classifier per each dataset through the loop. Here's the pseudo code of my program.</p>
  <pre><code>for dataset in total_data:
     X_train, X_test, y_train, y_test = Somefunction(dataset)
     model = XGBClassifier()
     model.fit(X_train, y_train)
  </code></pre>
  <p>Here, label(<code>y_train</code>) have one of the values <code>[0,1,2]</code>.  However, I just find that  <code>y_train</code> only have 1 and 2 in some datasets.  And because the XGBoost force label starts with 0, it yields an error.</p>
  <p>Is there any elegant way to tell XGBoost that there are the 3 kinds of labels even if only two exist in the train set?</p>
  <p>+)
  I try to use <code>num_class=3</code> as argument of XGBClassfier, but it yields another kind of error : <code>Invalid shape of labels</code></p>