False negative rate is the proportion of actual positive examples that a model incorrectly predicts as negative. It measures how often a system misses positives and is especially important in high-stakes detection tasks.
The proportion of actual positive examples for which the model mistakenly predicted the negative class. The following formula calculates the false negative rate: See Thresholds and the confusion matrix in Machine Learning Crash Course for more information.