Mean Absolute Error (MAE): MAE measures the average magnitude of the errors in a set of data. It is calculated by taking the average of the absolute differe...
Mean Absolute Error (MAE):
MAE measures the average magnitude of the errors in a set of data. It is calculated by taking the average of the absolute differences between the predicted values and the actual values.
Mean Squared Error (MSE):
MSE measures the average of the squared differences between the predicted values and the actual values. It is calculated by taking the average of the squared differences, where the difference is the difference between the predicted value and the actual value.
Root Mean Squared Error (RMSE):
RMSE is the square root of the average of the squared differences between the predicted values and the actual values. It is the square root of the MSE, and it is often used as a measure of the average magnitude of the errors.
Examples:
MAE: If the predicted values of a model are consistently higher than the actual values, the MAE will be high.
MSE: If the predicted values of a model are scattered around the actual values, the MSE will be high.
RMSE: If the predicted values of a model have a wide range of values, the RMSE will be high