Classification Model

Accuracy is not a good metric to use when you have class imbalance. The problem here is that an accuracy of 99% sounds like a great result, whereas your model performs very poorly. The goal of the F1 score is to combine the precision and recall metrics into a single metric. the F1 score has been designed to work well on imbalanced data.