KNN Classifier报Unknown label type: 'continuous'错误代码问题排查

核心问题 #

你的任务是预测连续型目标值，属于回归场景，但错误使用了K近邻分类器KNeighborsClassifier：

• 分类器仅支持离散类别标签作为训练目标，你传入的y是连续数值类型，因此触发Unknown label type: 'continuous'的标签类型不匹配错误。
• 回归场景需要使用KNeighborsRegressor（K近邻回归器）完成建模。

修正方案 #

仅需要替换KNN相关的导入和实例化代码即可，其余逻辑无需改动：

# training data
x = np.arange(0 , 5 , 0.05)
f_x = beta0 + beta1 * x + beta2 * x**2 + beta3 * x**3
epsilon = np.random.normal(loc=0, scale=sigma, size=100)
y = f_x + epsilon

## test data
x_test = np.arange(0 , 6, 0.1)
f_x_test = beta0 + beta1 * x_test + beta2 * x_test**2 + beta3 * x_test**3
epsilon_test = np.random.normal(loc=0, scale=sigma, size=len(x_test))
y_test = f_x_test + epsilon_test

z = np.random.multivariate_normal(size = 100, mean=[0]*20, cov=np.identity(20))
z_test = np.random.multivariate_normal(size = 60, mean=[0]*20, cov=np.identity(20))
train_x = np.concatenate((np.expand_dims(x, axis = 1),z),axis = 1)
test_x =  np.concatenate((np.expand_dims(x_test, axis = 1),z_test),axis = 1)
# 替换分类器为回归器
from sklearn.neighbors import KNeighborsRegressor
from sklearn import preprocessing
knn = KNeighborsRegressor(n_neighbors = 15)
from sklearn.metrics import mean_squared_error
knn.fit(train_x,y)
y_pred_train = knn.predict(train_x)
y_pred_test = knn.predict(test_x)
mse_train = mean_squared_error(y,y_pred_train)
mse_test = mean_squared_error(y_test,y_pred_test)

内容的提问来源于stack exchange，提问作者analyticsaspirant