Why split classification errors into false positives an...

If you pool errors, the feedback tracks the net difference between false positives and false negatives. If these counts are similar, the signal disappears even though the model is still making errors. Separate directions allow the optimizer to address specific instruction content for each error type, leading to faster and more stable convergence.