  The minimization of specific cases in binary classification, such as false
negatives or false positives, grows increasingly important as humans begin to
implement more machine learning into current products. While there are a few
methods to put a bias towards the reduction of specific cases, these methods
aren't very effective, hence their minimal use in models. To this end, a new
method is introduced to reduce the False Negatives or False positives without
drastically changing the overall performance or F1 score of the model. This
method involving the careful change to the real value of the input after
pre-training the model. Presenting the results of this method being applied on
various datasets, some being more complex than others. Through experimentation
on multiple model architectures on these datasets, the best model was found. In
all the models, an increase in the recall or precision, minimization of False
Negatives or False Positives respectively, was shown without a large drop in F1
score.
