我自己实现了一个自定义估算器，但我无法使用cross_val_score()函数，我认为这可能与我的predict()方法有关。以下是完整的错误跟踪信息：
Traceback (most recent call last): File "/Users/joann/Desktop/Implementações ML/Adaboost Classifier/test.py", line 30, in <module> ada2_score = cross_val_score(ada_2, X, y, cv=5) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 390, in cross_val_score error_score=error_score) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 236, in cross_validate for train, test in cv.split(X, y, groups)) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 1004, in __call__ if self.dispatch_one_batch(iterator): File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 835, in dispatch_one_batch self._dispatch(tasks) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 754, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 209, in apply_async result = ImmediateResult(func) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 590, in __init__ self.results = batch() File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 256, in __call__ for func, args, kwargs in self.items] File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/joblib/parallel.py", line 256, in <listcomp> for func, args, kwargs in self.items] File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 544, in _fit_and_score test_scores = _score(estimator, X_test, y_test, scorer) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py", line 591, in _score scores = scorer(estimator, X_test, y_test) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_scorer.py", line 89, in __call__ score = scorer(estimator, *args, **kwargs) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_scorer.py", line 371, in _passthrough_scorer return estimator.score(*args, **kwargs) File "/Users/joann/Desktop/Implementações ML/Adaboost Classifier/Adaboost.py", line 92, in score scr_pred = self.predict(X) File "/Users/joann/Desktop/Implementações ML/Adaboost Classifier/Adaboost.py", line 73, in predict clf_pred = clf.predict(X) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn_extensions/extreme_learning_machines/elm.py", line 614, in predict class_predictions = self.binarizer.inverse_transform(raw_predictions) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py", line 528, in inverse_transform self.classes_, threshold) File "/Users/joann/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py", line 750, in _inverse_binarize_thresholding format(y.shape))ValueError: output_type='binary', but y.shape = (30, 3)我的predict(self, X)方法返回一个大小为n_samples的向量，其中包含对X参数的预测。我还编写了一个score()函数如下：
def score(self, X, y): scr_pred = self.predict(X) return sum(scr_pred == y) / X.shape[0]这个方法只是计算给定样本的模型准确率。无论我使用这个score()方法还是设置cross_val_score(... , scoring="accuracy")，都无法正常工作。
注意：我知道这个问题/答案，但这不适用于我的情况，因为我可以确认我的构造函数的一致性：
def __init__(self, estimators=["MLP"], n_rounds=5, random_state=10): self.estimators = estimators self.n_rounds = n_rounds self.random_state = random_state更新：
进一步的研究让我找到了这个话题，其中解释了sklearn无法深度复制带有转换器的估算器。然而，我的估算器必须运行LabelBinarizer来转换数据以获取预测。因此，我更新了问题标题以反映实际问题。
回答：
然而，你的问题陈述在这里并不清晰，但从错误来看，似乎你在尝试进行多类分类。
问题在于，你的代码中可能在某个地方没有正确进行预处理，因为错误是从inverse_binarize_thresholding记录的，这是由于sklearn预处理的以下功能引起的：
def _inverse_binarize_thresholding(y, output_type, classes, threshold): if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2: raise ValueError("output_type='binary', but y.shape = {0}". format(y.shape))你的代码中一定缺少了一些转换或预处理，你需要正确使用LabelBinarizer。
请参考以下文档并回溯错误以修复你的代码。
