title: XGBoost produce prediction result and probability
body: <p>I am probably looking right over it in the documentation, but I wanted to know if there is a way with XGBoost to generate both the prediction and probability for the results? In my case, I am trying to predict a multi-class classifier. it would be great if I could return Medium - 88%. </p>
  
  <ul>
  <li>Classifier = Medium </li>
  <li>Probability of Prediction = 88%</li>
  </ul>
  
  <p>parameters</p>
  
  <pre><code>params = {
      'max_depth': 3,
      'objective': 'multi:softmax',  # error evaluation for multiclass training
      'num_class': 3,
      'n_gpus': 0
  }
  </code></pre>
  
  <p>prediction</p>
  
  <pre><code>pred = model.predict(D_test)
  </code></pre>
  
  <p>results</p>
  
  <pre><code>array([2., 2., 1., ..., 1., 2., 2.], dtype=float32)
  </code></pre>
  
  <p>User friendly (label encoder)</p>
  
  <pre><code>pred_int = pred.astype(int)
  label_encoder.inverse_transform(pred_int[:5])
  array(['Medium', 'Medium', 'Low', 'Low', 'Medium'], dtype=object)
  </code></pre>
  
  <p><strong>EDIT:</strong>
  @Reveille suggested predict_proba. I am not instantiating XGBClassifer(). Should I be? How would I modify my pipeline to use that, if so?</p>
  
  <pre><code>params = {
      'max_depth': 3,
      'objective': 'multi:softmax',  # error evaluation for multiclass training
      'num_class': 3,
      'n_gpus': 0
  }
  
  steps = 20  # The number of training iterations
  
  model = xgb.train(params, D_train, steps)
  </code></pre>