The average squared difference between classifier predicted output and actual output.

The measure described, which represents the average squared difference between the predicted output of a classifier and the actual output, is known as Option A: mean squared error. Mean squared error is a common metric used to evaluate the performance of machine learning models, with lower values indicating better predictive accuracy.

Option B: root mean squared error is a closely related metric that represents the square root of the mean squared error. It is also used for assessing model performance, and it provides a measure in the same units as the original data.

Option C: mean absolute error measures the average absolute difference between predicted and actual values, but it does not square the differences as in mean squared error.

Option D: mean relative error is not a standard metric for measuring prediction accuracy and is not commonly used in the context of machine learning.

In conclusion, the correct term for the described measure is Option A: mean squared error.