False Negatives
In the context of data science and artificial intelligence, false negatives refer to the instances where a model fails to identify a positive outcome when it actually exists. This means that the model incorrectly predicts a negative outcome when it should have predicted a positive outcome. False negatives can occur in various applications such as medical diagnosis, fraud detection, and spam filtering. False negatives can be detrimental in certain applications, such as in medical diagnosis, where a false negative can result in a missed diagnosis and delayed treatment, leading to serious consequences. In order to reduce the number of false negatives, it is important to optimize the model's sensitivity and recall, and to carefully select the threshold for classification.
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