我正在尝试为我的多类分类模型计算交叉验证的精度得分。为此，我使用了make_scorer，并将平均值设置为加权平均。
#cross-validated precisionfrom sklearn.metrics import make_scorerscorer = make_scorer(average_precision_score, average = 'weighted')scorer然而，当我运行代码时，我得到了以下错误：
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-170-b2ce1e8a452e> in <module> 2 from sklearn.metrics import make_scorer 3 scorer = make_scorer(average_precision_score, average = 'weighted')----> 4 cv_precision = cross_val_score(clf, X, y, cv=5, scoring=scorer) 5 cv_precision = np.mean(cv_prevision) 6 cv_precision~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score) 388 fit_params=fit_params, 389 pre_dispatch=pre_dispatch,--> 390 error_score=error_score) 391 return cv_results['test_score'] 392 ~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score) 234 return_times=True, return_estimator=return_estimator, 235 error_score=error_score)--> 236 for train, test in cv.split(X, y, groups)) 237 238 zipped_scores = list(zip(*scores))~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable) 1002 # remaining jobs. 1003 self._iterating = False-> 1004 if self.dispatch_one_batch(iterator): 1005 self._iterating = self._original_iterator is not None 1006 ~\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator) 833 return False 834 else:--> 835 self._dispatch(tasks) 836 return True 837 ~\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch) 752 with self._lock: 753 job_idx = len(self._jobs)--> 754 job = self._backend.apply_async(batch, callback=cb) 755 # A job can complete so quickly than its callback is 756 # called before we get here, causing self._jobs to~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback) 207 def apply_async(self, func, callback=None): 208 """Schedule a func to be run"""--> 209 result = ImmediateResult(func) 210 if callback: 211 callback(result)~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch) 588 # Don't delay the application, to avoid keeping the input 589 # arguments in memory--> 590 self.results = batch() 591 592 def get(self):~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs)--> 256 for func, args, kwargs in self.items] 257 258 def __len__(self):~\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs)--> 256 for func, args, kwargs in self.items] 257 258 def __len__(self):~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py 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, error_score) 542 else: 543 fit_time = time.time() - start_time--> 544 test_scores = _score(estimator, X_test, y_test, scorer) 545 score_time = time.time() - start_time - fit_time 546 if return_train_score:~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _score(estimator, X_test, y_test, scorer) 589 scores = scorer(estimator, X_test) 590 else:--> 591 scores = scorer(estimator, X_test, y_test) 592 593 error_msg = ("scoring must return a number, got %s (%s) "~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in __call__(self, estimator, *args, **kwargs) 85 if isinstance(scorer, _BaseScorer): 86 score = scorer._score(cached_call, estimator,---> 87 *args, **kwargs) 88 else: 89 score = scorer(estimator, *args, **kwargs)~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in _score(self, method_caller, estimator, X, y_true, sample_weight) 210 else: 211 return self._sign * self._score_func(y_true, y_pred,--> 212 **self._kwargs) 213 214 ~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in average_precision_score(y_true, y_score, average, pos_label, sample_weight) 213 pos_label=pos_label) 214 return _average_binary_score(average_precision, y_true, y_score,--> 215 average, sample_weight=sample_weight) 216 217 ~\anaconda3\lib\site-packages\sklearn\metrics\_base.py in _average_binary_score(binary_metric, y_true, y_score, average, sample_weight) 72 y_type = type_of_target(y_true) 73 if y_type not in ("binary", "multilabel-indicator"):---> 74 raise ValueError("{0} format is not supported".format(y_type)) 75 76 if y_type == "binary":ValueError: multiclass format is not supported此外，当我尝试引入交叉验证时，它也不起作用…
from sklearn.metrics import make_scorerscorer = make_scorer(average_precision_score, average = 'weighted')cv_precision = cross_val_score(clf, X, y, cv=5, scoring=scorer)cv_precision = np.mean(cv_prevision)cv_precision我得到了相同的错误。
回答：
