我正在处理一个使用 ColumnTransformer 和 LabelEncoder 对著名的泰坦尼克数据集 X 进行预处理的示例：
Age Embarked Fare Sex0 22.0 S 7.2500 male1 38.0 C 71.2833 female2 26.0 S 7.9250 female3 35.0 S 53.1000 female4 35.0 S 8.0500 male像这样调用转换器：
from sklearn.compose import ColumnTransformerfrom sklearn.preprocessing import LabelEncoderColumnTransformer( transformers=[ ("label-encode categorical", LabelEncoder(), ["Sex", "Embarked"]) ]).fit(X).transform(X)结果是：
---------------------------------------------------------------------------TypeError Traceback (most recent call last)<ipython-input-54-fd5a05b7e47e> in <module> 4 ("label-encode categorical", LabelEncoder(), ["Sex", "Embarked"]) 5 ]----> 6 ).fit(X).transform(X)~/anaconda3/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py in fit(self, X, y) 418 # we use fit_transform to make sure to set sparse_output_ (for which we 419 # need the transformed data) to have consistent output type in predict--> 420 self.fit_transform(X, y=y) 421 return self 422 ~/anaconda3/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py in fit_transform(self, X, y) 447 self._validate_remainder(X) 448 --> 449 result = self._fit_transform(X, y, _fit_transform_one) 450 451 if not result:~/anaconda3/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py in _fit_transform(self, X, y, func, fitted) 391 _get_column(X, column), y, weight) 392 for _, trans, column, weight in self._iter(--> 393 fitted=fitted, replace_strings=True)) 394 except ValueError as e: 395 if "Expected 2D array, got 1D array instead" in str(e):~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable) 915 # remaining jobs. 916 self._iterating = False--> 917 if self.dispatch_one_batch(iterator): 918 self._iterating = self._original_iterator is not None 919 ~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator) 757 return False 758 else:--> 759 self._dispatch(tasks) 760 return True 761 ~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch) 714 with self._lock: 715 job_idx = len(self._jobs)--> 716 job = self._backend.apply_async(batch, callback=cb) 717 # A job can complete so quickly than its callback is 718 # called before we get here, causing self._jobs to~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback) 180 def apply_async(self, func, callback=None): 181 """Schedule a func to be run"""--> 182 result = ImmediateResult(func) 183 if callback: 184 callback(result)~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch) 547 # Don't delay the application, to avoid keeping the input 548 # arguments in memory--> 549 self.results = batch() 550 551 def get(self):~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self) 223 with parallel_backend(self._backend, n_jobs=self._n_jobs): 224 return [func(*args, **kwargs)--> 225 for func, args, kwargs in self.items] 226 227 def __len__(self):~/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0) 223 with parallel_backend(self._backend, n_jobs=self._n_jobs): 224 return [func(*args, **kwargs)--> 225 for func, args, kwargs in self.items] 226 227 def __len__(self):~/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, **fit_params) 612 def _fit_transform_one(transformer, X, y, weight, **fit_params): 613 if hasattr(transformer, 'fit_transform'):--> 614 res = transformer.fit_transform(X, y, **fit_params) 615 else: 616 res = transformer.fit(X, y, **fit_params).transform(X)TypeError: fit_transform() takes 2 positional arguments but 3 were given**fit_params 这里的问题是什么？对我来说这看起来像是 sklearn 的一个错误，或者至少是不兼容的情况。
回答：
有两个主要原因导致这个方法无法用于您的目的。
LabelEncoder()设计用于目标变量（y）。这就是当columnTransformer()尝试传入X, y=None, fit_params={}时会引发位置参数错误的原因。
来自 文档：
使用0到n_classes-1之间的值编码标签。
fit(y)
拟合标签编码器参数:
y : 形状为 (n_samples,) 的数组类型
目标值。
- 即使您找到一个解决方案来移除空字典，
LabelEncoder()也无法处理二维数组（基本上是多个特征），因为它只能处理一维的y值。
简短回答 – 我们不应该使用 LabelEncoder() 来处理输入特征。
那么，如何对输入特征进行编码呢？
如果您的特征是序数特征，请使用 OrdinalEncoder()，如果是名义特征，则使用 OneHotEncoder()。
示例：
>>> from sklearn.compose import ColumnTransformer>>> from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder>>> X = np.array([[1000., 100., 'apple', 'green'],... [1100., 100., 'orange', 'blue']])>>> ct = ColumnTransformer(... [("ordinal", OrdinalEncoder(), [0, 1]), ("nominal", OneHotEncoder(), [2, 3])])>>> ct.fit_transform(X) array([[0., 0., 1., 0., 0., 1.], [1., 0., 0., 1., 1., 0.]]) 