title: did i use lgbm correctly for categorical features?
body: <p>Suppose my data has only 1 column of categorical feature (already converted it to astype(int)),</p>
  
  <pre><code>x_to_use = x_to_use.astype(int)
  </code></pre>
  
  <p>I am using this in a sklearn Pipeline:</p>
  
  <pre><code>                                LGBMRegressor(**{
                                              "n_estimators":100,
                                      "categorical_feature":(0,),
                                              "max_depth":1,
                                          }
  </code></pre>
  
  <p>I got this warning.</p>
  
  <pre><code>c:\programdata\miniconda3\lib\site-packages\lightgbm\basic.py:842: UserWarning: categorical_feature keyword has been found in `params` an
  Please use categorical_feature argument of the Dataset constructor to pass this parameter.
  </code></pre>
  
  <p>I want everything to be in standard sklearn machine learning pipeline format, so my input is always np.array. Is my pipeline construction correct or is                                  "categorical_feature":(0,), simply ignored and that column is not treated as categorical.</p>
  
  <p><strong>Edit:</strong></p>
  
  <p>OK... I constructed a simple test, "categorical_feature" is actually used correctly.</p>