Emphasis on the Minimization of False Negatives or False Positives in Binary Classification
This addresses a practical issue for developers implementing machine learning in products where specific error types are critical, though it appears incremental.
The paper tackles the problem of minimizing false negatives or false positives in binary classification without significantly harming overall model performance, showing an increase in recall or precision without a large drop in F1 score across various datasets and model architectures.
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.