code: def _fit_data(self, X):
          
  
          if self.binarize is not None:
              X = binarize(X, threshold=self.binarize)
  
          for i in range(X.shape[1]):
  
              # initialise binarizer and save
              binarizer = LabelBinarizer()
  
              # fit the data to the binarizer
              binarizer.fit(X[:, i])
              self._binarizers.append(binarizer)
  
          return self._transform_data(X)
docs: Binarize the data for each column separately.
  
          Parameters
          ----------
          X : array-like, shape = [n_samples, n_features]
  
          Returns
          -------
          X_transformed : array-like
              Returns the data where in each columns the labels are
              binarized.