title: How to predict X using XGB load_model?
body: <p>I'm creating a model by doing:</p>
  
  <pre><code>X=merged_total.values
  Y=X[:,0]
  X=X[:,[1,2,5,7,8,9,10,11,12]]
  
  from xgboost import XGBRegressor
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  
  
  X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.125)
  # fit model no training data
  model = XGBRegressor()
  model.fit(X_train, y_train)
  # make predictions for test data
  y_pred = model.predict(X_test)
  model.save_model('Civil.model')
  </code></pre>
  
  <p>and later in the code, when I try to load and predict Y for X_test doing:</p>
  
  <pre><code>X=keytable.values
  Y=X[:,13]
  X=X[:,[8,7,6,15,13,14,12,2,0]]
  
  
  
  X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.125)
  # # fit model no training data
  model = XGBRegressor()
  model = model.load_model('Civil.model')
  model.fit(X_train, y_train)
  # # make predictions for test data
  y_pred = model.predict(X_test)
  </code></pre>
  
  <p>I always get the error "AttributeError: 'NoneType' object has no attribute 'predict'/'fit'"
  What am I doing wrong?</p>
  
  <p>I'm using the github cloned version of XGB on a MacBook Air.</p>