랜덤 포레스트 오류와 관련한 질문입니다

fit하는 과정에서 문제가 생긴 것 같고 몇달 전 저와 같은 질문을 하신분이 계셨는데 그분에게 답변해주신 것을 보고 따라해보려해도 해결이 안돼서 질문 남깁니다 
어떤식으로 해결해야할 지 설명해주시면 감사하겠습니다 ㅜㅜ!


from sklearn.model_selection import GridSearchCV

params = {
    'n_estimators':[100],
    'max_depth' : [6, 8, 10, 12], 
    'min_samples_leaf' : [8, 12, 18 ],
    'min_samples_split' : [8, 16, 20]
}
# RandomForestClassifier 객체 생성 후 GridSearchCV 수행
rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)
grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1 )
grid_cv.fit(X_train )

print('최적 하이퍼 파라미터:\n', grid_cv.best_params_)
print('최고 예측 정확도: {0:.4f}'.format(grid_cv.best_score_))

---------------------------------------------------------------------------
UnicodeEncodeError                        Traceback (most recent call last)
<ipython-input-45-a4ef1b9de222> in <module>
     10 rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1)
     11 grid_cv = GridSearchCV(rf_clf , param_grid=params , cv=2, n_jobs=-1 )
---> 12 grid_cv.fit(X_train , y_train,encoding='UTF-8')
     13 
     14 print('최적 하이퍼 파라미터:\n', grid_cv.best_params_)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    777                                     verbose=self.verbose)
    778         results = {}
--> 779         with parallel:
    780             all_candidate_params = []
    781             all_out = []

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __enter__(self)
    728     def __enter__(self):
    729         self._managed_backend = True
--> 730         self._initialize_backend()
    731         return self
    732 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in _initialize_backend(self)
    738         """Build a process or thread pool and return the number of workers"""
    739         try:
--> 740             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
    741                                              **self._backend_args)
    742             if self.timeout is not None and not self._backend.supports_timeout:

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
    492                 SequentialBackend(nesting_level=self.nesting_level))
    493 
--> 494         self._workers = get_memmapping_executor(
    495             n_jobs, timeout=idle_worker_timeout,
    496             env=self._prepare_worker_env(n_jobs=n_jobs),

C:\ProgramData\Anaconda3\lib\site-packages\joblib\executor.py in get_memmapping_executor(n_jobs, **kwargs)
     18 
     19 def get_memmapping_executor(n_jobs, **kwargs):
---> 20     return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)
     21 
     22 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\executor.py in get_memmapping_executor(cls, n_jobs, timeout, initializer, initargs, env, temp_folder, context_id, **backend_args)
     40         _executor_args = executor_args
     41 
---> 42         manager = TemporaryResourcesManager(temp_folder)
     43 
     44         # reducers access the temporary folder in which to store temporary

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in __init__(self, temp_folder_root, context_id)
    529             # exposes exposes too many low-level details.
    530             context_id = uuid4().hex
--> 531         self.set_current_context(context_id)
    532 
    533     def set_current_context(self, context_id):

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in set_current_context(self, context_id)
    533     def set_current_context(self, context_id):
    534         self._current_context_id = context_id
--> 535         self.register_new_context(context_id)
    536 
    537     def register_new_context(self, context_id):

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in register_new_context(self, context_id)
    558                 new_folder_name, self._temp_folder_root
    559             )
--> 560             self.register_folder_finalizer(new_folder_path, context_id)
    561             self._cached_temp_folders[context_id] = new_folder_path
    562 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_memmapping_reducer.py in register_folder_finalizer(self, pool_subfolder, context_id)
    588         # semaphores and pipes
    589         pool_module_name = whichmodule(delete_folder, 'delete_folder')
--> 590         resource_tracker.register(pool_subfolder, "folder")
    591 
    592         def _cleanup():

C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in register(self, name, rtype)
    189         '''Register a named resource, and increment its refcount.'''
    190         self.ensure_running()
--> 191         self._send('REGISTER', name, rtype)
    192 
    193     def unregister(self, name, rtype):

C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in _send(self, cmd, name, rtype)
    202 
    203     def _send(self, cmd, name, rtype):
--> 204         msg = '{0}:{1}:{2}\n'.format(cmd, name, rtype).encode('ascii')
    205         if len(name) > 512:
    206             # posix guarantees that writes to a pipe of less than PIPE_BUF

UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)