How to optimize the ratio of (True positive)/(False postive) instead of accuracy?

Question

The classic metrics is "accuracy", which is related to： (True positive + True negative)/(False positive + False negative)

In a classification problem, False negative is more tolerable than false positive. That is, I want to assign more weight to improving (True positive)/(False positive). How to accomplish this?

model.compile(optimizer='adam',
          loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
          metrics=['accuracy'])

Answer 1

Tensorflow allows sensitivities to be shifted for these metrics https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity , or if you want the false positives directly (which I think only gives you access to the number of false positives if that helps): https://www.tensorflow.org/api_docs/python/tf/keras/metrics/FalsePositives . I do not know much about tensorflow but I hope this helps