분류 예측에서 결과값의 구체적 내용을 확인할수 있는지요?

선생님, 감사합니다. 분류문제의 라벨인코딩의 에러 메시지의 원인 및 해결을 하고 싶습니다.

from sklearn.preprocessing import LabelEncoder

X_label = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town', 'alone']

X_train[label] = X_train[label].apply(LabelEncoder().fit_transform)

X_test[label] = X_test[label].apply(LabelEncoder().fit_transform)

print(X_train.head())

[에러 메시지]

ypeError                                 Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode(values, uniques, encode, check_unknown)
    111         try:
--> 112             res = _encode_python(values, uniques, encode)
    113         except TypeError:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode_python(values, uniques, encode)
     59     if uniques is None:
---> 60         uniques = sorted(set(values))
     61         uniques = np.array(uniques, dtype=values.dtype)

TypeError: '<' not supported between instances of 'float' and 'str'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-14-d8de3af8ea81> in <module>
      1 from sklearn.preprocessing import LabelEncoder
      2 X_label = ['sex', 'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town', 'alone']
----> 3 X_train[label] = X_train[label].apply(LabelEncoder().fit_transform)
      4 X_test[label] = X_test[label].apply(LabelEncoder().fit_transform)
      5 print(X_train.head())

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
   6876             kwds=kwds,
   6877         )
-> 6878         return op.get_result()
   6879 
   6880     def applymap(self, func) -> "DataFrame":

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\apply.py in get_result(self)
    184             return self.apply_raw()
    185 
--> 186         return self.apply_standard()
    187 
    188     def apply_empty_result(self):

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\apply.py in apply_standard(self)
    311 
    312         # compute the result using the series generator
--> 313         results, res_index = self.apply_series_generator()
    314 
    315         # wrap results

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\apply.py in apply_series_generator(self)
    339         else:
    340             for i, v in enumerate(series_gen):
--> 341                 results[i] = self.f(v)
    342                 keys.append(v.name)
    343 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in fit_transform(self, y)
    250         """
    251         y = column_or_1d(y, warn=True)
--> 252         self.classes_, y = _encode(y, encode=True)
    253         return y
    254 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\preprocessing\_label.py in _encode(values, uniques, encode, check_unknown)
    112             res = _encode_python(values, uniques, encode)
    113         except TypeError:
--> 114             raise TypeError("argument must be a string or number")
    115         return res
    116     else:

TypeError: argument must be a string or number

안녕하세요.

숫자가 어떤 종을 나타내는 것인지 확인할 코드를 제공해드립니다.

다른 방법으로는 새로운 데이터 프레임을 만들어 확인이 가능합니다.

강의의 라벨인코딩 과정에서  아래 코드를 추가하면 데이터 프레임으로 확인을 할 수 있습니다.