title: eli5 show_prediction not showing probability
body: <p>I'm using the <code>show_prediction</code> function in the <code>eli5</code> package to understand how my XGBoost classifier arrived at a prediction. For some reason I seem to be getting a regression score instead of a probability for my model.</p>
  
  <p>Below is a fully reproducible example with a public dataset.</p>
  
  <pre><code>from sklearn.datasets import load_breast_cancer
  from xgboost import XGBClassifier
  from sklearn.model_selection import train_test_split
  from eli5 import show_prediction
  
  # Load dataset
  data = load_breast_cancer()
  
  # Organize our data
  label_names = data['target_names']
  labels = data['target']
  feature_names = data['feature_names']
  features = data['data']
  
  
  # Split the data
  train, test, train_labels, test_labels = train_test_split(
      features,
      labels,
      test_size=0.33,
      random_state=42
  )
  
  # Define the model
  xgb_model = XGBClassifier(
      n_jobs=16,
      eval_metric='auc'
  )
  
  # Train the model
  xgb_model.fit(
      train,
      train_labels
  )
  
  show_prediction(xgb_model.get_booster(), test[0], show_feature_values=True, feature_names=feature_names)
  </code></pre>
  
  <p>This gives me the following result. Note the score of 3.7, which is definitely not a probability.</p>
  
  <p><a href="https://i.stack.imgur.com/WhkFC.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/WhkFC.png" alt="enter image description here"></a></p>
  
  <p>The official eli5 <a href="https://eli5.readthedocs.io/en/latest/tutorials/xgboost-titanic.html#explaining-predictions" rel="nofollow noreferrer">documentation</a> correctly shows a probability though.</p>
  
  <p><a href="https://i.stack.imgur.com/eStzL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/eStzL.png" alt="enter image description here"></a></p>
  
  <p>The missing probability seems to be related to my use of <code>xgb_model.get_booster()</code>. Looks like the official documentation doesn't use that and passes the model as-is instead, but when I do that I get <code>TypeError: 'str' object is not callable</code>, so that doesn't seem to be an option.</p>
  
  <p>I'm also concerned that eli5 is not explaining the prediction by traversing the xgboost trees. It appears that the "score" I'm getting is actually just a sum of all the feature contributions, like I would expect if eli5 wasn't actually traversing the tree but fitting a linear model instead. Is that true? How can I also make eli5 traverse the tree?</p>