PYTHON

Root Mean Squared Error

# Needed packages
from sklearn.metrics import mean_squared_error

#  Values to compare
y_true = [[0.5, 1],[-1, 1],[7, -6]]
y_pred = [[0, 2],[-1, 2],[8, -5]]

# Root mean squared error (by using: squared=False)

rmse = mean_squared_error(y_true, y_pred, squared=False)

print(rmse)

Mean Squared Error

# Needed packages
from sklearn.metrics import mean_squared_error

#  Values to compare
y_true = [3, -0.5, 2, 7] # Observed value
y_pred = [2.5, 0.0, 2, 8] # Predicted value

# Mean squared error
mse = mean_squared_error(y_true, y_pred)

print(mse)

root mean squared error in machine learning formula

from sklearn.metrics import mean_squared_error
from math import sqrt

actual_values = [3, -0.5, 2, 7]
predicted_values = [2.5, 0.0, 2, 8]

mean_squared_error(actual_values, predicted_values)
# taking root of mean squared error
root_mean_squared_error = sqrt(mean_squared_error)