ValueError：未知标签类型：“连续” - KNeighborsClassifier

如何解决ValueError：未知标签类型：“连续” - KNeighborsClassifier

我通过 SKLearn 参加了有关监督学习的 DataCamp 课程。我完成了一部分，我想通过打开 Jupyter Notebook 并使用 KNeighborsClassifier 进行一些分类来测试我的新技能。

运行此代码时出现错误 ValueError: UnkNown label type: 'continuous'：

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('data.csv').drop('street',axis=1).drop('city',axis=1).drop('statezip',axis=1).drop('country',axis=1)
X = df.drop('price',axis=1)
y = df['price']
counter = 0
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=21)
# print(len(X_train))
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X_train,y_train)

错误：

ValueError: UnkNown label type: 'continuous'
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-23-ca9d16d8c630> in <module>
      5 # print(len(X_train))
      6 knn = KNeighborsClassifier(n_neighbors=6)
----> 7 knn.fit(X_train,y_train)

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_classification.py in fit(self,X,y)
    177             The fitted k-nearest neighbors classifier.
    178         """
--> 179         return self._fit(X,y)
    180 
    181     def predict(self,X):

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_base.py in _fit(self,y)
    379                     self.outputs_2d_ = True
    380 
--> 381                 check_classification_targets(y)
    382                 self.classes_ = []
    383                 self._y = np.empty(y.shape,dtype=int)

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/multiclass.py in check_classification_targets(y)
    181     if y_type not in ['binary','multiclass','multiclass-multIoUtput',182                       'multilabel-indicator','multilabel-sequences']:
--> 183         raise ValueError("UnkNown label type: %r" % y_type)
    184 
    185 

ValueError: UnkNown label type: 'continuous'

我该如何解决这个问题？

（我见过很多问同样问题的问题，但我要么没有正确理解解决方案，要么解决方案完全不同。）