For our metrics, we want to include precision and recall as they will provide use with a more informed picture of how good our model is. Accuracy tells us what fraction of the labels is correct. Since our data is not balanced, accuracy might give a skewed sense of a good model (i.e. a model that always predicts PNEUMONIA will be 74% accurate but is not a good model).