Model evaluation is very important in data science. It helps you to understand the performance of your model and makes it easy to present your model to other people.
MSE- MSE is calculated by the sum of square of prediction error which is real output minus predicted output and then divide by the number of data points.It gives you an absolute number on how much your predicted results deviate from the actual number. You cannot interpret many insights from one single result but it gives you a real number to compare against other model results and help you select the best regression model.
RMSE- It gives you an absolute number on how much your predicted results deviate from the actual number. You cannot interpret many insights from one single result but it gives you a real number to compare against other model results and help you select the best regression model.
MAE- Mean Absolute Error(MAE) is similar to Mean Square Error(MSE). However, instead of the sum of square of error in MSE, MAE is taking the sum of the absolute value of error. MAE is a more direct representation of sum of error terms. MSE gives larger penalization to big prediction error by squaring it while MAE treats all errors the same.