交叉验证函数返回“未知标签类型：(array([0.0, 1.0], dtype=object),)”

以下是完整的错误：

`---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[33], line 2
      1 gnb = GaussianNB()
----> 2 cv = cross_val_score(gnb,X_train,y_train,cv=5, error_score = 'raise')
      3 print(cv)
      4 print(cv.mean())

File /opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:515, in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
    512 # To ensure multimetric format is not supported
    513 scorer = check_scoring(estimator, scoring=scoring)
--> 515 cv_results = cross_validate(
    516     estimator=estimator,
    517     X=X,
    518     y=y,
    519     groups=groups,
    520     scoring={"score": scorer},
    521     cv=cv,
    522     n_jobs=n_jobs,
    523     verbose=verbose,
    524     fit_params=fit_params,
    525     pre_dispatch=pre_dispatch,
    526     error_score=error_score,
    527 )
    528 return cv_results["test_score"]

File /opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:266, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
    263 # We clone the estimator to make sure that all the folds are
    264 # independent, and that it is pickle-able.
    265 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 266 results = parallel(
    267     delayed(_fit_and_score)(
    268         clone(estimator),
    269         X,
    270         y,
    271         scorers,
    272         train,
    273         test,
    274         verbose,
    275         None,
    276         fit_params,
    277         return_train_score=return_train_score,
    278         return_times=True,
    279         return_estimator=return_estimator,
    280         error_score=error_score,
    281     )
    282     for train, test in cv.split(X, y, groups)
    283 )
    285 _warn_or_raise_about_fit_failures(results, error_score)
    287 # For callabe scoring, the return type is only know after calling. If the
    288 # return type is a dictionary, the error scores can now be inserted with
    289 # the correct key.

File /opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:63, in Parallel.__call__(self, iterable)
     58 config = get_config()
     59 iterable_with_config = (
     60     (_with_config(delayed_func, config), args, kwargs)
     61     for delayed_func, args, kwargs in iterable
     62 )
---> 63 return super().__call__(iterable_with_config)

File /opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1918, in Parallel.__call__(self, iterable)
   1916     output = self._get_sequential_output(iterable)
   1917     next(output)
-> 1918     return output if self.return_generator else list(output)
   1920 # Let's create an ID that uniquely identifies the current call. If the
   1921 # call is interrupted early and that the same instance is immediately
   1922 # re-used, this id will be used to prevent workers that were
   1923 # concurrently finalizing a task from the previous call to run the
   1924 # callback.
   1925 with self._lock:

File /opt/conda/lib/python3.10/site-packages/joblib/parallel.py:1847, in Parallel._get_sequential_output(self, iterable)
   1845 self.n_dispatched_batches += 1
   1846 self.n_dispatched_tasks += 1
-> 1847 res = func(*args, **kwargs)
   1848 self.n_completed_tasks += 1
   1849 self.print_progress()

File /opt/conda/lib/python3.10/site-packages/sklearn/utils/parallel.py:123, in _FuncWrapper.__call__(self, *args, **kwargs)
    121     config = {}
    122 with config_context(**config):
--> 123     return self.function(*args, **kwargs)

File /opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:686, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    684         estimator.fit(X_train, **fit_params)
    685     else:
--> 686         estimator.fit(X_train, y_train, **fit_params)
    688 except Exception:
    689     # Note fit time as time until error
    690     fit_time = time.time() - start_time

File /opt/conda/lib/python3.10/site-packages/sklearn/naive_bayes.py:267, in GaussianNB.fit(self, X, y, sample_weight)
    265 self._validate_params()
    266 y = self._validate_data(y=y)
--> 267 return self._partial_fit(
    268     X, y, np.unique(y), _refit=True, sample_weight=sample_weight
    269 )

File /opt/conda/lib/python3.10/site-packages/sklearn/naive_bayes.py:427, in GaussianNB._partial_fit(self, X, y, classes, _refit, sample_weight)
    424 if _refit:
    425     self.classes_ = None
--> 427 first_call = _check_partial_fit_first_call(self, classes)
    428 X, y = self._validate_data(X, y, reset=first_call)
    429 if sample_weight is not None:

File /opt/conda/lib/python3.10/site-packages/sklearn/utils/multiclass.py:420, in _check_partial_fit_first_call(clf, classes)
    413             raise ValueError(
    414                 "`classes=%r` is not the same as on last call "
    415                 "to partial_fit, was: %r" % (classes, clf.classes_)
    416             )
    418     else:
    419         # This is the first call to partial_fit
--> 420         clf.classes_ = unique_labels(classes)
    421         return True
    423 # classes is None and clf.classes_ has already previously been set:
    424 # nothing to do

File /opt/conda/lib/python3.10/site-packages/sklearn/utils/multiclass.py:107, in unique_labels(*ys)
    105 _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
    106 if not _unique_labels:
--> 107     raise ValueError("Unknown label type: %s" % repr(ys))
    109 if is_array_api:
    110     # array_api does not allow for mixed dtypes
    111     unique_ys = xp.concat([_unique_labels(y) for y in ys])

ValueError: Unknown label type: (array([0.0, 1.0], dtype=object),)`

我正在尝试在 Kaggle 上的泰坦尼克号太空船项目中实现交叉验证，您可以在此处找到我的完整笔记本 我无法运行交叉验证功能。我已经摆弄了几个小时，但一直找不到解决方案。 我按照

Ken Jee 的泰坦尼克号项目示例 完成了我自己的 泰坦尼克号预测提交 在此代码中，交叉验证函数可以成功运行，我尝试将其重新用于泰坦尼克号太空船项目。 如果您需要任何其他详细信息，请告诉我。我对机器学习很陌生，所以我提前感谢您的耐心。

我已经尝试摆脱空值，验证我的训练和测试列表的数据类型是否相同，并且我已经尝试将分类变量映射到数值变量。

I've tried getting rid of null values, verified that the data types for my training and test lists are the same, and I've tried mapping categorical variables to numerical variables.

遇到的错误“ValueError：未知标签类型：(array([0.0, 1.0], dtype=object),)”表明的目标变量（ y_train ）数据类型存在问题。尽管该数组包含数字，但其数据类型为 object ，这表示它可能包含混合类型或被视为字符串。Scikit-learn 期望目标变量为数值类型（通常为整数）用于分类。

以下是解决此问题的几个步骤：

1. 检查数据类型： 在调用 cross_val_score 函数之前，请打印 y_train 的数据类型以确认其是否为 object 。 python print(y_train.dtype)

2. 转换数据类型： 如果数据类型为 object ，则需要将其转换为数值类型，例如 int 或 float 。可以使用 astype 方法执行此操作： python y_train = y_train.astype(int) 如果的目标变量包含字符串标签（例如“是”、“否”），则需要先使用 LabelEncoder 将它们转换为数值： python from sklearn.preprocessing import LabelEncoder le = LabelEncoder() y_train = le.fit_transform(y_train)

3. 在数据源处查找问题： 错误的数据类型可能表示数据加载或预处理过程中存在问题。仔细检查从CSV文件或其他数据源加载数据的方式，并确保目标列包含预期类型的值。

4. 检查空值： 确保的目标变量 y_train 中没有空值或NaN值。可以使用以下方法处理空值：

5. 删除包含空值的行。
6. 使用平均值、中位数或最频繁值等策略填充空值。

通过执行这些步骤，应该能够解决“未知标签类型”错误并成功使用 cross_val_score 函数。