When using gradient descent to train a linear classifier, why don't we use accuracy as a loss function? Which of these options and why?

It is only reliable in situations with high class imbalance.

The confidence interval is high on small test sets.

It causes the loss landscape to be flat almost everywhere

It makes the loss zero almost everywhere.