我用下面的代码来进行特性排名和一些交叉验证。

from sklearn.tree import DecisionTreeRegressor

dtr = DecisionTreeRegressor(random_state =  42)
# Train model
model = dtr.fit(X_airbnb, y_airbnb)

feat_importances = pd.DataFrame(model.feature_importances_, index = X_airbnb.columns, columns=["Importance"])
feat_importances.sort_values(by='Importance', ascending=False, inplace=True)

output = cross_validate(dtr, X_airbnb, y_airbnb, cv=2, scoring = 'accuracy', return_estimator =True)

for idx,estimator in enumerate(output['estimator']):
    print("Features sorted by their score for estimator {}:".format(idx))
    feature_importances = pd.DataFrame(estimator.feature_importances_,
                                       index = X_airbnb.columns,
                                        columns=['importance']).sort_values('importance', ascending=False)
    print(feature_importances)

输出给出了特性排名，但也给出了ValueError：不支持连续。完整错误代码如下

d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\model_selection\_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\model_selection\_validation.py", line 767, in _score
    scores = scorer(estimator, X_test, y_test)
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\metrics\_scorer.py", line 220, in __call__
    return self._score(
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\metrics\_scorer.py", line 268, in _score
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\utils\_param_validation.py", line 192, in wrapper
    return func(*args, **kwargs)
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\metrics\_classification.py", line 221, in accuracy_score
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)
  File "d:\ITU\CalculusandStatistics\KDS_Statistics_GroupProject\venv\lib\site-packages\sklearn\metrics\_classification.py", line 106, in _check_targets
    raise ValueError("{0} is not supported".format(y_type))
ValueError: continuous is not supported

接着是所需的输出，然后是错误的重复。
主要的问题是我看不出结果有什么问题。我想把feature_importances保存到数据框中，但是它不允许。