Intuitively it is not as easy to understand as accuracy but f1 is usually more useful than accuracy especially if you have an uneven class distribution. F1 score combines precision and recall relative to a specific positive class the f1 score can be interpreted as a weighted average of the precision and recall where an f1 score reaches its best value at 1 and worst at 0.