Ways to Reduce False Positive or False Negatives in Binary Classification (0,1) [closed]

1. How do you minimize false positive and false negatives?
2. How do you reduce the number of false negatives?

How do you minimize false positive and false negatives?

To minimize the number of False Negatives (FN) or False Positives (FP) we can also retrain a model on the same data with slightly different output values more specific to its previous results. This method involves taking a model and training it on a dataset until it optimally reaches a global minimum.

How do you reduce the number of false negatives?

Current methods that are available to minimize cases like false negatives include weight change, performing data aug- mentation to create a biased dataset, and changing the decision boundary line [2].