The false positive rate is the probability of incorrectly classifying a negative instance as positive in a classification model. This metric is essential for understanding the performance of a model, particularly in contexts where the cost of false alarms is significant. A high false positive rate can lead to unnecessary interventions or misallocation of resources, affecting overall decision-making processes.
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