from sklearn.metrics import mean_squared_error

y_actual = [1, 2, 3]
y_predicted = [1.5, 2.5, 3.5]
rmse = mean_squared_error(y_actual, y_predicted, squared=False)
print(rmse)  # 0.5

d7c7a3594b26772ba167b2046ad493da45860e99
<mask token>

<mask token>
print(rmse)

<mask token>
y_actual = [1, 2, 3]
y_predicted = [1.5, 2.5, 3.5]
rmse = mean_squared_error(y_actual, y_predicted, squared=False)
print(rmse)

from sklearn.metrics import mean_squared_error
y_actual = [1, 2, 3]
y_predicted = [1.5, 2.5, 3.5]
rmse = mean_squared_error(y_actual, y_predicted, squared=False)
print(rmse)

from sklearn.metrics import mean_squared_error

y_actual = [1, 2, 3]
y_predicted = [1.5, 2.5, 3.5]
rmse = mean_squared_error(y_actual, y_predicted, squared=False)
print(rmse)  # 0.5
