Kaggle太空泰坦尼克项目cross_val_score报错未知标签类型

我在Kaggle的太空泰坦尼克项目中实现交叉验证时，cross_val_score函数始终无法运行，报错信息为：Unknown label type: (array([0.0, 1.0], dtype=object),)。

完整报错栈：

---------------------------------------------------------------------------
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),)

我参考Ken Jee的泰坦尼克项目示例（该示例中交叉验证可正常运行）进行适配，先后尝试处理空值、验证训练集与测试集数据类型一致、将分类变量映射为数值变量等操作，但问题仍未解决。

核心原因 #

报错的关键在于标签y_train的数据类型是object而非数值类型（如int或float），Scikit-learn的分类模型无法识别object类型的标签。从报错信息里的array([0.0, 1.0], dtype=object)可以看出，标签值虽然是0.0和1.0，但被存储为了object类型。

修复步骤 #

1. 转换标签数据类型：将y_train转换为int或float类型，推荐使用int（因为是二分类任务，标签为0和1）。
   执行代码：

   y_train = y_train.astype(int)
   # 或者如果需要保留浮点型：y_train = y_train.astype(float)
   

2. 验证转换结果：转换后可以打印y_train.dtype确认类型是否正确：

   print(y_train.dtype)  # 应输出int64或float64
   

3. 重新运行交叉验证：转换完成后再执行cross_val_score即可正常运行。

额外注意事项 #

• 确保测试集的标签（如果有）也执行同样的类型转换，避免后续预测时出现问题。
• 在数据预处理阶段，处理完空值和分类变量后，务必检查所有特征和标签的数据类型，确保符合模型的输入要求。

内容的提问来源于stack exchange，提问作者nicklaus-slade