问题描述
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。)
解决方法
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