How can I explain the interpretation of the root mean squared error?

In the context of data science, the root mean squared error is a commonly used metric to measure the average deviation of the predicted values from the actual values in a regression problem like as a prediction of the prices of houses. It is calculated by taking the square root of the Average of the squared differences between predicted and actual values.

For example, let’s say you have a regression model for predicting house prices:-

From sklearn.metrics import mean_squared_error

Import numpy as np

# Assuming y_true and y_pred are the actual and predicted house prices respectively
Mse = mean_squared_error(y_true, y_pred)
Rmse = np.sqrt(mse)
Print(“RMSE:”, rmse)