title: xgboost predict_proba : How to do the mapping between the probabilities and the labels
body: <p>I'm trying to predict solve a multiclass classification using the xgboost algorithm, however i do not know how does <code>predict_proba</code> works exactly. In fact, <code>predict_proba</code> generates a list of probabilities but i don't know to which class each probability is related.</p>
  
  <p>Here is a simple example:</p>
  
  <p>This my train data:</p>
  
  <pre><code>+------------+----------+-------+
  | feature1   | feature2 | label |
  +------------+----------+-------+
  |    x       |    z     |   3   |
  +------------+----------+-------+
  |    y       |    u     |   0   |
  +------------+----------+-------+
  |    x       |    u     |   2   |
  +------------+----------+-------+
  </code></pre>
  
  <p>Then when I try to predict probas for a new example</p>
  
  <pre><code>model.predict_proba(['x','u'])
  </code></pre>
  
  <p>This will return something like this:</p>
  
  <pre><code>[0.2, 0.3, 0.5]
  </code></pre>
  
  <p>My question is : what is the class that has the probability of 0.5 ? is it the class 2, or 3 or 0 ? </p>