title: XGBoost error - When categorical type is supplied, DMatrix parameter `enable_categorical` must be set to `True`
body: <p>I have four categorial features and a fifth numerical one (Var5).  When I try the following code:</p>
  <pre><code>cat_attribs = ['var1','var2','var3','var4']
  
  full_pipeline = ColumnTransformer([('cat', OneHotEncoder(handle_unknown = 'ignore'), cat_attribs)], remainder = 'passthrough')
  X_train = full_pipeline.fit_transform(X_train)
  
  model = XGBRegressor(n_estimators=10, max_depth=20, verbosity=2)
  model.fit(X_train, y_train)
  y_pred = model.predict(X_test)
  </code></pre>
  <p>I get the following error message when the model tries to make its predictions:</p>
  <blockquote>
  <p>ValueError: DataFrame.dtypes for data must be int, float, bool or categorical.  When
  categorical type is supplied, DMatrix parameter
  <code>enable_categorical</code> must be set to <code>True</code>.Var1, Var2, Var3, Var4</p>
  </blockquote>
  <p>Does anyone know what's going wrong here?</p>
  <p>In case it's helpful, here is a small sample of the X_train data and the y_train data:</p>
  <pre><code>       Var1  Var2  Var3 Var4        Var5
  1507856   JP  2009  6581  OME  325.787218
  839624    FR  2018  5783  I_S   11.956326
  1395729   BE  2015  6719  OME   42.888565
  1971169   DK  2011  3506  RPP   70.094146
  1140120   AT  2019  5474  NMM  270.082738
  </code></pre>
  <p>and:</p>
  <pre><code>          Ind_Var
  1507856   8.013558
  839624    4.105559
  1395729   7.830077
  1971169  83.000000
  1140120  51.710526
  </code></pre>