How to reduce false negatives in logistic regression

1. How can false negatives be reduced?
2. How to reduce false negatives in logistic regression Python?
3. How do you reduce false positives and negatives?

How can false negatives be reduced?

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].

How to reduce false negatives in logistic regression Python?

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 false positives and negatives?

In order to reduce false positives and false negatives, take care when combining matching algorithms and configuring them based on language, scenarios and company policies. You should use different matching algorithms that account for different cases.