from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
iris = load_boston()
X = iris.data
y = iris.target
tree = KNeighborsRegressor()
tree.fit(X, y)
p = tree.predict(X)
print(X)
print(y)
print(p)
print(mean_squared_error(y, p))

18941efad07abd000375829c9c9b126b352b54d8
<mask token>

<mask token>
tree.fit(X, y)
<mask token>
print(X)
print(y)
print(p)
print(mean_squared_error(y, p))

<mask token>
iris = load_boston()
X = iris.data
y = iris.target
tree = KNeighborsRegressor()
tree.fit(X, y)
p = tree.predict(X)
print(X)
print(y)
print(p)
print(mean_squared_error(y, p))

from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
iris = load_boston()
X = iris.data
y = iris.target
tree = KNeighborsRegressor()
tree.fit(X, y)
p = tree.predict(X)
print(X)
print(y)
print(p)
print(mean_squared_error(y, p))
