以下是代码:
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
任何人都可以帮助我吗?
答案 0 :(得分:0)
如果要预测连续变量,则需要回归而不是分类。 KNeighborsRegressor与KNeighborsClassifier。