ValueError: Unknown label type: while using cross_validation

Question

代码如下：

import pandas as pd
import numpy as np
from sklearn.cross_validation import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv',index_col = 0)
X = data[['TV','Radio','Newspaper']]
y = data[['Sales']]
y = np.asarray(y)
y = np.ravel(y)
knn = KNeighborsClassifier(n_neighbors = 5)
scores = cross_val_score(knn,X,y,cv=10,scoring = 'accuracy')
print(scores)

我收到以下错误

C:\Users\Kunal Desai\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
171     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput', 
172             'multilabel-indicator', 'multilabel-sequences']:
--> 173         raise ValueError("Unknown label type: %r" % y)
174 
175 

ValueError: Unknown label type:

我是 cross_validation 和 scikit-learn

的新手

谁能帮帮我？

Answer 1

如果你想预测一个连续变量，你需要回归而不是分类。 KNeighborsRegressor 与 KNeighborsClassifier。