我试图确定KNN和随机森林之间预测模型的准确度得分，但准确度得分方法给出了主题行中给出的误差。我的代码如下：

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(x_train,y_train)
knn.predict(x_test)
#Accuracy of prediction
y_pred = knn.predict(x_test)
#predictions = [round(value) for value in y_pred]
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))    

上述精度打印报表的输出为：

Accuracy: 80.04%

现在是第二种方法：

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=100, min_samples_leaf=10,     
random_state=1)
model.fit(x_train, y_train)
print(model.score)
#Accuracy of prediction
y_pred = model.predict(x_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

在随机森林的情况下，精度评分函数给出以下误差

accuracy = accuracy_score(y_test, y_pred)
Traceback (most recent call last):

File "<ipython-input-263-232dbb7449ea>", line 1, in <module>
accuracy = accuracy_score(y_test, y_pred)

File "C:\Anaconda3\lib\site-packages\sklearn\metrics\classification.py",    
line 172, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)

File "C:\Anaconda3\lib\site-packages\sklearn\metrics\classification.py",   
line 82, in _check_targets
"".format(type_true, type_pred))
ValueError: Can't handle mix of multilabel-indicator and continuous- 
multioutput

为什么在第二个分类器的情况下，精度_分数会给出该值错误？ValueError:无法处理多标签指示器和连续多输出精度_分数（）的混合

任何建议都非常感谢！