parameters: (self)
return_statement: return self._best_iteration
docstring: Get the best iteration of fitted model.
docstring_summary: Get the best iteration of fitted model.
docstring_tokens: Get the best iteration of fitted model .
function: def best_iteration_(self):
          """Get the best iteration of fitted model."""
          if self._n_features is None:
              raise LGBMNotFittedError('No best_iteration found. Need to call fit with early_stopping_rounds beforehand.')
          return self._best_iteration
function_tokens: def best_iteration_ ( self ) : if self . _n_features is None : raise LGBMNotFittedError ( 'No best_iteration found. Need to call fit with early_stopping_rounds beforehand.' ) return self . _best_iteration