Python for Machine Learning - The Complete Beginners Course - Evaluating the Performance of the Regression Model

The video tutorial explains the performance metrics used to evaluate models, focusing on Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). MAE is calculated by taking the mean of the absolute values of the errors, providing a sense of the model's accuracy. MSE involves squaring the errors before averaging, while RMSE is the square root of MSE, offering a different perspective on error magnitude.

How do you interpret the value obtained from the mean absolute error?

Describe the process of calculating the mean squared error (MSE).