The correct answer is A. mean squared error (MSE).
MSE is a measure of the difference between the predicted and actual values. It is calculated by taking the average of the squared differences between the predicted and actual values.
MSE is a good measure of the accuracy of a classifier. A low MSE indicates that the classifier is accurate, while a high MSE indicates that the classifier is inaccurate.
B. root mean squared error (RMSE) is the square root of MSE. It is a measure of the spread of the errors. A low RMSE indicates that the errors are small, while a high RMSE indicates that the errors are large.
C. mean absolute error (MAE) is the average of the absolute values of the differences between the predicted and actual values. It is a measure of the magnitude of the errors. A low MAE indicates that the errors are small, while a high MAE indicates that the errors are large.
D. mean relative error (MRE) is the average of the absolute values of the differences between the predicted and actual values divided by the actual values. It is a measure of the relative magnitude of the errors. A low MRE indicates that the errors are small relative to the actual values, while a high MRE indicates that the errors are large relative to the actual values.